Annotation
literal: Ontology of Core Data Mining Entities

Annotation
literal: true

Annotation
literal: Saso Dzeroski

Annotation
literal: 0.2

Annotation
literal: Pance Panov

Annotation
literal: English

Annotation
literal: 2.0

Annotation
literal: Pance Panov

Annotation
literal: Ana Kostovska

Annotation
literal: Data Mining and Knowledge Discovery

Annotation
literal: OntoDM-core

Annotation
literal: OntoDM-core

Annotation
literal: English

Annotation
literal: Pance Panov

Annotation
literal: Larisa Soldatova

Annotation
literal: OWL-DL

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000002
literal: constraints_set

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000005
literal: pre_processing_algorithm_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000018
literal: numeric_datatype

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000024
literal: data_object

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000025
literal: In the case of learning probabilistic models, we need to find a mapping of the form m::Td-->(Tc-->R0+). In the more general formulation of the problem, the training examples can have probability distributions over the c values insdead having the c values by itself.

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000025
literal: probabilistic_predictive_modeling_task

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000032
literal: prototype_calculation_process

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000035
literal: search_function_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000040
literal: distance_function_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000046
literal: distance_calculation_process

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000056
literal: evaluation_method_parameter

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000062
literal: refinement_relation_generation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000064
literal: primitive_feature_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000073
literal: Probabilistic predictive model P for types Td and Tc is a function that takes an object of type Td and return a probabiliy distribution over type Tc, i.e. has the signature p::Tc-->(Td-->R0+)

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000073
literal: probabilistic_predictive_model

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000079
literal: refinement_operator_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000086
literal: feature_generation_process

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000115
literal: data_values

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000117
literal: basic_data_mining_component_role

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000123
literal: post_processing_task

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000129
literal: generalization_role

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000138
literal: base_learners_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000139
literal: post_processing_algorithm_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000146
literal: have to specify the output here!

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000146
literal: kernel_calculation_process

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000148
literal: information_content_entity_agregate

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000152
literal: pre_processing_algorithm_implementation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000153
literal: target_feature_set_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000157
literal: kernel_representation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000164
literal: post_processing_algorithm_implementation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000171
literal: example_representation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000171
literal: data_item_representation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000174
literal: refinement_operator_representation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000176
literal: assignment_process

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000180
literal: fold_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000187
literal: feature_generation_role

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000188
literal: structured_feature_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000190
literal: Describes set of parameters

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000190
literal: parameters_set_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000194
literal: datatype_representation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000203
literal: dataset_role

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000208
literal: parameter_role

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000218
literal: bag_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000221
literal: function_representation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000222
literal: data_role

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000236
literal: scenario_representation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000245
literal: projection_objective

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000257
literal: assesement specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000274
literal: pre_processing_task

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000277
literal: stuctured_datatype_construction

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000278
literal: An interpreter takes as input the data part of a pattern and an example and returns the result of applying the function part of the pattern to the example.

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000278
literal: interpretation_process

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000279
literal: descriptive_feature_set_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000284
literal: dataset_projection_process

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000289
literal: data_use_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000295
literal: query_representation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000296
literal: prototype_function_representation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000301
literal: kernel_function_specification

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000304
literal: distance_representation

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000307
literal: unlabeled_example

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000307
literal: unlabeled_data_item

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_000310
literal: dataset_projection

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_251805
literal: OntoDM_251805

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM#OntoDM_251805
literal: matematical function

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_269620
literal: OntoDM_269620s

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_269620
literal: OntoDM_269620

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_269620
literal: precentage train-test split evaluation algorithm

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_349672
literal: OntoDM_349672s

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_349672
literal: OntoDM_349672

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_349672
literal: separate test set evaluation algorithm

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_361694
literal: OntoDM_361694s

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_361694
literal: OntoDM_361694

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_361694
literal: cross validation evaluation algorithm

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_562975
literal: OntoDM_562975s

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_562975
literal: OntoDM_562975

AnnotationAssertion
IRI: http://kt.ijs.si/panovp/OntoDM.owl#OntoDM_562975
literal: train set evaluation algorithm

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_018988
literal: OntoDT_018988s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_018988
literal: OntoDT_018988

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_150012
literal: OntoDT_150012s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_150012
literal: OntoDT_150012

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_245924
literal: OntoDT_245924s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_245924
literal: OntoDT_245924

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_360708
literal: OntoDT_360708s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_360708
literal: OntoDT_360708

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_377271
literal: OntoDT_377271s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_377271
literal: OntoDT_377271

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_378476
literal: OntoDT_378476s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_378476
literal: OntoDT_378476

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_379444
literal: OntoDT_379444s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_379444
literal: OntoDT_379444

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_471356
literal: OntoDT_471356s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_471356
literal: OntoDT_471356

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_487147
literal: OntoDT_487147s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_487147
literal: OntoDT_487147

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_521859
literal: OntoDT_521859s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_521859
literal: OntoDT_521859

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_591397
literal: OntoDT_591397s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_591397
literal: OntoDT_591397

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_595741
literal: OntoDT_595741s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_595741
literal: OntoDT_595741

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_596819
literal: OntoDT_596819s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_596819
literal: OntoDT_596819

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_608148
literal: OntoDT_608148s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_608148
literal: OntoDT_608148

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_645869
literal: OntoDT_645869s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_645869
literal: OntoDT_645869

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_648082
literal: OntoDT_648082s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_648082
literal: OntoDT_648082

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_699579
literal: OntoDT_699579s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_699579
literal: OntoDT_699579

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_814373
literal: OntoDT_814373s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_814373
literal: OntoDT_814373

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_878284
literal: OntoDT_878284s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_878284
literal: OntoDT_878284

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_950419
literal: OntoDT_950419s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_950419
literal: OntoDT_950419

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_974622
literal: OntoDT_974622s

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_974622
literal: OntoDT_974622

AnnotationAssertion
IRI: http://ontodm.com/OntoDT#OntoDT_991825
literal: OntoDT_991825

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000022
literal: specified_input_role

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000023
literal: specified_output_role

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000069
literal: OBI_0000069s

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000069
literal: OBI_0000069

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000260
literal: OBI_0000260s

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000260
literal: OBI_0000260

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000260
literal: plan

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000283
literal: Annotation property imported from OBI to denote terms that have been imported from other ontologies

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000283
literal: imported_from

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000587
literal: OBI_0000587s

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000587
literal: OBI_0000587

AnnotationAssertion
IRI: http://purl.obofoundry.org/obo/OBI_0000587
literal: protocol_paricipant_role

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/BFO_0000017
literal: RealizableEntities

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/BFO_0000017
literal: RealizableEntity

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/BFO_0000019
literal: Qualities

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/BFO_0000019
literal: Quality

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/BFO_0000023
literal: Roles

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/BFO_0000023
literal: Role

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000005
literal: IAO_0000005s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000005
literal: IAO_0000005

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000009
literal: IAO_0000009s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000009
literal: IAO_0000009

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000010
literal: IAO_0000010s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000010
literal: IAO_0000010

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000025
literal: IAO_0000025s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000025
literal: IAO_0000025

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000027
literal: IAO_0000027s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000027
literal: IAO_0000027

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000028
literal: IAO_0000028s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000028
literal: IAO_0000028

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000030
literal: IAO_0000030s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000030
literal: IAO_0000030

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000033
literal: IAO_0000033s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000033
literal: IAO_0000033

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000033
literal: directive information entity

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000064
literal: IAO_0000064s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000064
literal: IAO_0000064

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000065
literal: IAO_0000065s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000065
literal: IAO_0000065

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000088
literal: IAO_0000088s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000088
literal: IAO_0000088

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000096
literal: IAO_0000096s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000096
literal: IAO_0000096

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000098
literal: IAO_0000098s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000098
literal: IAO_0000098

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000100
literal: IAO_0000100s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000100
literal: IAO_0000100

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000104
literal: IAO_0000104s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000104
literal: IAO_0000104

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000124
literal: IAO_0000124s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000124
literal: IAO_0000124

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000129
literal: IAO_0000129s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000129
literal: IAO_0000129

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000136
literal: IAO_0000136

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000136
literal: IAO_0000136s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000136
literal: IAO_0000136ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000136
literal: is_about

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000300
literal: IAO_0000300s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000300
literal: IAO_0000300

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000302
literal: IAO_0000302s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000302
literal: IAO_0000302

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000310
literal: IAO_0000310s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000310
literal: IAO_0000310

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000310
literal: document

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000311
literal: IAO_0000311s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000311
literal: IAO_0000311

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000314
literal: IAO_0000314s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000314
literal: IAO_0000314

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000314
literal: document part

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000418
literal: IAO_0000418

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000418
literal: IAO_0000418s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000418
literal: IAO_0000418ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000442
literal: OntoDM_247286s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000442
literal: OntoDM_247286

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000442
literal: agent role

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000572
literal: IAO_0000572s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000572
literal: IAO_0000572

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000572
literal: a planned process of capturing information in an enduring form with the intent to communicate this information

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/IAO_0000572
literal: documenting

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000011
literal: OBI_0000011s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000011
literal: OBI_0000011

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000047
literal: OBI_0000047s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000047
literal: OBI_0000047

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000047
literal: Is a material entity that is created or changed during material processing.

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000047
literal: OBI

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000047
literal: OBI consortium

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000047
literal: processed material

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000227
literal: OBI_0000227s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000227
literal: OBI_0000227

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000245
literal: OBI_0000245s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000245
literal: OBI_0000245

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000272
literal: OBI_0000272s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000272
literal: OBI_0000272

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000272
literal: protocol

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000293
literal: OBI_0000293

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000293
literal: OBI_0000293s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000293
literal: OBI_0000293ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000293
literal: has_specified_input

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000294
literal: OBI_0000294

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000294
literal: OBI_0000294s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000294
literal: OBI_0000294ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000294
literal: Is a relationship between a generically dependent continuant and a specifically dependent continuant. A generically dependent continuant may inhere in more than one entity. It does so by virtue of the fact that there is, for each entity that it inheres, a specifically dependent *concretization* of the generically dependent continuant that is specifically dependent. For instance, consider a story, which is an information artifact that inheres in some number of books. Each book bears some quality that carries the story. The relation between this quality and the generically dependent continuant is that the former is the concretization of the latter.

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000294
literal: is_concretization_of

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000295
literal: OBI_0000295

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000295
literal: OBI_0000295s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000295
literal: OBI_0000295ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000295
literal: is_specified_input_of

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000297
literal: OBI_0000297

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000297
literal: OBI_0000297s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000297
literal: OBI_0000297ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000297
literal: Is a relationship between a specifically dependent continuant and a generically dependent continuant. A generically dependent continuant may inhere in more than one entity. It does so by virtue of the fact that there is, for each entity that it inheres, a specifically dependent *concretization* of the generically dependent continuant that is specifically dependent. For instance, consider a story, which is an information artifact that inheres in some number of books. Each book bears some quality that carries the story. The relation between this quality and the generically dependent continuant is that the former is the concretization of the latter.

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000297
literal: is_concretized_as

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000298
literal: OBI_0000298

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000298
literal: OBI_0000298s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000298
literal: OBI_0000298ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000298
literal: has_quality

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000299
literal: OBI_0000299

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000299
literal: OBI_0000299s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000299
literal: OBI_0000299ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000299
literal: has_specified_output

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000300
literal: OBI_0000300

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000300
literal: OBI_0000300s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000300
literal: OBI_0000300ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000300
literal: Relation between a realizable and a process. Reciprocal relation of realizes [GOC:cjm]

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000300
literal: is_realized_by

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000304
literal: OBI_0000304

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000304
literal: OBI_0000304s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000304
literal: OBI_0000304ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000304
literal: is_manufactured_by

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000308
literal: OBI_0000308

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000308
literal: OBI_0000308s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000308
literal: OBI_0000308ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000308
literal: Relation between a process and a function, where the unfolding of the process requires the execution of the function. Class level: P realizes F iff: given any p that instantiates P, there exists some f, t such that f instantiates F at t and p *realizes* f. Here, *realizes* is the primitive instance level relation [GOC:cjm]

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000308
literal: realizes

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000312
literal: OBI_0000312

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000312
literal: OBI_0000312s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000312
literal: OBI_0000312ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000312
literal: is_specified_output_of

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000316
literal: OBI_0000316

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000316
literal: OBI_0000316s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000316
literal: OBI_0000316ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000316
literal: has_role

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000338
literal: OBI_0000338s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000338
literal: OBI_0000338

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000338
literal: the process of evaluating the data gathered in an investigation in the context of literature knowledge with the objective to generate more general conclusions or to identify what additional data is necessary to draw conclusions

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000338
literal: interpreting data

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000339
literal: OBI_0000339s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000339
literal: OBI_0000339

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000339
literal: planning

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000417
literal: OBI_0000417

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000417
literal: OBI_0000417s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000417
literal: OBI_0000417ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000417
literal: achieves_planned_objective

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000658
literal: OBI_0000658s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000658
literal: OBI_0000658

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000833
literal: OBI_0000833

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000833
literal: OBI_0000833s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000833
literal: OBI_0000833ed

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000833
literal: objective_achieved_by

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000968
literal: OBI_0000968s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000968
literal: OBI_0000968

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000968
literal: A device is a processed material which is designed to perform some function or functions

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000968
literal: OBI

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000968
literal: OBI consortium

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0000968
literal: device

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0302911
literal: OBI_0302911s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0302911
literal: OBI_0302911

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0400107
literal: OBI_0400107s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0400107
literal: OBI_0400107

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0400107
literal: A computer is an instrument which manipulates (stores, retrieves, and processes) data according to a list of instructions.

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0400107
literal: OBI

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0400107
literal: OBI consortium

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0400107
literal: computer

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0600008
literal: OBI_0600008s

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0600008
literal: OBI_0600008

AnnotationAssertion
IRI: http://purl.obolibrary.org/obo/OBI_0600008
literal: acquisition

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#bearer_of
literal: bearer_of

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#bearer_of
literal: bearer_ofs

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#bearer_of
literal: bearer_ofed

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#bearer_of
literal: bearer_of

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#inheres_in
literal: inheres_in

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#inheres_in
literal: inhereses_in

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#inheres_in
literal: inheresed_in

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#inheres_in
literal: OntoDM_462848

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#inheres_in
literal: inheres_in

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#quality_of
literal: quality_of

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#quality_of
literal: quality_ofs

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#quality_of
literal: quality_ofed

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#quality_of
literal: quality_of

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#role_of
literal: role_of

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#role_of
literal: role_ofs

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#role_of
literal: role_ofed

AnnotationAssertion
IRI: http://purl.org/obo/owl/OBO_REL#role_of
literal: role_of

AnnotationAssertion
IRI: http://www.ebi.ac.uk/efo/swo/SWO_0000393
literal: SWO_0000393s

AnnotationAssertion
IRI: http://www.ebi.ac.uk/efo/swo/SWO_0000393
literal: SWO_0000393

AnnotationAssertion
IRI: http://www.ebi.ac.uk/efo/swo/SWO_0000393
literal: Information processing is a process in which input information is analysed or transformed in order to produce information as output.

AnnotationAssertion
IRI: http://www.ebi.ac.uk/efo/swo/SWO_0000393
literal: James Malone

AnnotationAssertion
IRI: http://www.ebi.ac.uk/efo/swo/SWO_0000393
literal: information processing

AnnotationAssertion
IRI: http://www.hozo.jp/owl/EXPOApr19.xml/has_representation
literal: has_representation

AnnotationAssertion
IRI: http://www.hozo.jp/owl/EXPOApr19.xml/has_representation
literal: has_representations

AnnotationAssertion
IRI: http://www.hozo.jp/owl/EXPOApr19.xml/has_representation
literal: has_representationed

AnnotationAssertion
IRI: http://www.hozo.jp/owl/EXPOApr19.xml/has_representation
literal: has_representation

AnnotationAssertion
IRI: http://www.ifomis.org/bfo/1.1/span#ProcessualEntity
literal: ProcessualEntities

AnnotationAssertion
IRI: http://www.ifomis.org/bfo/1.1/span#ProcessualEntity
literal: ProcessualEntity

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#agent_in
literal: agent_in

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#agent_in
literal: agents_in

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#agent_in
literal: agented_in

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#has_agent
literal: has_agent

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#has_agent
literal: has_agents

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#has_agent
literal: has_agented

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#has_part
literal: has_part

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#has_part
literal: has_parts

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#has_part
literal: has_parted

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#has_participant
literal: has_participant

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#has_participant
literal: has_participants

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#has_participant
literal: has_participanted

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#part_of
literal: part_of

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#part_of
literal: part_ofs

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#part_of
literal: part_ofed

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#participates_in
literal: participates_in

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#participates_in
literal: participateses_in

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#participates_in
literal: participatesed_in

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#preceded_by
literal: preceded_by

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#preceded_by
literal: preceded_bies

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#preceded_by
literal: preceded_bied

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#precedes
literal: precedes

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#precedes
literal: precedeses

AnnotationAssertion
IRI: http://www.obofoundry.org/ro/ro.owl#precedes
literal: precedesed

AnnotationAssertion
IRI: OntoDM_000000
literal: OntoDM_000000s

AnnotationAssertion
IRI: OntoDM_000000
literal: OntoDM_000000

AnnotationAssertion
IRI: OntoDM_000000
literal: prediction_error

AnnotationAssertion
IRI: OntoDM_000001
literal: OntoDM_000001s

AnnotationAssertion
IRI: OntoDM_000001
literal: OntoDM_000001

AnnotationAssertion
IRI: OntoDM_000001
literal: A predictive modelling ensemble is a realizable entity that is a concretization of a predictive model ensemble specification specification and it is a specified output of a predictive modeling algorithm execution.

AnnotationAssertion
IRI: OntoDM_000001
literal: predictive models ensemble

AnnotationAssertion
IRI: OntoDM_000002
literal: feature-based unlabeled data stream

AnnotationAssertion
IRI: OntoDM_000003
literal: structure-based unlabeled data stream

AnnotationAssertion
IRI: OntoDM_000004
literal: OntoDM_000004s

AnnotationAssertion
IRI: OntoDM_000004
literal: OntoDM_000004

AnnotationAssertion
IRI: OntoDM_000004
literal: A probability distribution ensemble is a realizable entity that is a concretization of a probability distribution ensemble specification and it is a specified output of a probability distribution estimation algorithm execution.

AnnotationAssertion
IRI: OntoDM_000004
literal: probability distribution ensemble

AnnotationAssertion
IRI: OntoDM_0000045
literal: OntoDM_0000045s

AnnotationAssertion
IRI: OntoDM_0000045
literal: OntoDM_0000045

AnnotationAssertion
IRI: OntoDM_0000045
literal: SubBag

AnnotationAssertion
IRI: OntoDM_000005
literal: transactional data stream

AnnotationAssertion
IRI: OntoDM_000006
literal: OntoDM_000006s

AnnotationAssertion
IRI: OntoDM_000006
literal: OntoDM_000006

AnnotationAssertion
IRI: OntoDM_000008
literal: OntoDM_000008s

AnnotationAssertion
IRI: OntoDM_000008
literal: OntoDM_000008

AnnotationAssertion
IRI: OntoDM_000008
literal: prototype

AnnotationAssertion
IRI: OntoDM_000009
literal: OntoDM_000009s

AnnotationAssertion
IRI: OntoDM_000009
literal: OntoDM_000009

AnnotationAssertion
IRI: OntoDM_000009
literal: probability distribution representation

AnnotationAssertion
IRI: OntoDM_000011
literal: OntoDM_000011s

AnnotationAssertion
IRI: OntoDM_000011
literal: OntoDM_000011

AnnotationAssertion
IRI: OntoDM_000011
literal: Set of distance functions is a collection of distance functions.

AnnotationAssertion
IRI: OntoDM_000011
literal: distance functions set

AnnotationAssertion
IRI: OntoDM_000014
literal: OntoDM_000014s

AnnotationAssertion
IRI: OntoDM_000014
literal: OntoDM_000014

AnnotationAssertion
IRI: OntoDM_000014
literal: Evaluation functions measure the validity of generalizations on a given set of data.

AnnotationAssertion
IRI: OntoDM_000014
literal: evaluation function specification

AnnotationAssertion
IRI: OntoDM_000015
literal: OntoDM_000015s

AnnotationAssertion
IRI: OntoDM_000015
literal: OntoDM_000015

AnnotationAssertion
IRI: OntoDM_000015
literal: An clustering ensemble is a realizable entity that is a concretization of a clusterings ensemble specification and it is a specified output of a clustering algorithm execution.

AnnotationAssertion
IRI: OntoDM_000015
literal: clustering ensemble

AnnotationAssertion
IRI: OntoDM_000016
literal: OntoDM_000016s

AnnotationAssertion
IRI: OntoDM_000016
literal: OntoDM_000016

AnnotationAssertion
IRI: OntoDM_000017
literal: OntoDM_000017s

AnnotationAssertion
IRI: OntoDM_000017
literal: OntoDM_000017

AnnotationAssertion
IRI: OntoDM_000017
literal: test set role

AnnotationAssertion
IRI: OntoDM_000020
literal: OntoDM_000020s

AnnotationAssertion
IRI: OntoDM_000020
literal: OntoDM_000020

AnnotationAssertion
IRI: OntoDM_000020
literal: A pattern P on type T is a boolean function on objects of type T, i.e has the signature p::T-->boolean.

AnnotationAssertion
IRI: OntoDM_000021
literal: OntoDM_000021s

AnnotationAssertion
IRI: OntoDM_000021
literal: OntoDM_000021

AnnotationAssertion
IRI: OntoDM_000021
literal: complex constraint

AnnotationAssertion
IRI: OntoDM_000022
literal: OntoDM_000022s

AnnotationAssertion
IRI: OntoDM_000022
literal: OntoDM_000022

AnnotationAssertion
IRI: OntoDM_000022
literal: pattern quality

AnnotationAssertion
IRI: OntoDM_000027
literal: OntoDM_000027s

AnnotationAssertion
IRI: OntoDM_000027
literal: OntoDM_000027

AnnotationAssertion
IRI: OntoDM_000027
literal: output datatype specification is a datatype specification that denotes the datatype of the data on the output part of the dataset. The output data is used for predictive modeling tasks.

AnnotationAssertion
IRI: OntoDM_000027
literal: output data specification

AnnotationAssertion
IRI: OntoDM_000029
literal: OntoDM_000029s

AnnotationAssertion
IRI: OntoDM_000029
literal: OntoDM_000029

AnnotationAssertion
IRI: OntoDM_000029
literal: predictive model evaluation function specification

AnnotationAssertion
IRI: OntoDM_000031
literal: OntoDM_000031s

AnnotationAssertion
IRI: OntoDM_000031
literal: OntoDM_000031

AnnotationAssertion
IRI: OntoDM_000031
literal: a dataset specification is a data item specification about a dataset defined with a datatype specification of the data examples aggregated in the dataset.

AnnotationAssertion
IRI: OntoDM_000031
literal: dataset specification

AnnotationAssertion
IRI: OntoDM_000033
literal: OntoDM_000033s

AnnotationAssertion
IRI: OntoDM_000033
literal: OntoDM_000033

AnnotationAssertion
IRI: OntoDM_000033
literal: data mining algorithm execution

AnnotationAssertion
IRI: OntoDM_000034
literal: OntoDM_000034s

AnnotationAssertion
IRI: OntoDM_000034
literal: OntoDM_000034

AnnotationAssertion
IRI: OntoDM_000034
literal: Generalization specification of a directive informational entity class that specifies the type of generalization. It includes information about the types of data used to produced the generalization and the language in which the generalization is expressed.

AnnotationAssertion
IRI: OntoDM_000034
literal: represents class of generalizations as in Dzeroski paper

AnnotationAssertion
IRI: OntoDM_000034
literal: generalization specification

AnnotationAssertion
IRI: OntoDM_000036
literal: OntoDM_000036s

AnnotationAssertion
IRI: OntoDM_000036
literal: OntoDM_000036

AnnotationAssertion
IRI: OntoDM_000036
literal: A predictive model M for types Td and Tc is a function that takes an object of type Td and returns and object of type Tc, i.e. has the signature m::Td-->Tc.

AnnotationAssertion
IRI: OntoDM_000036
literal: predictive model

AnnotationAssertion
IRI: OntoDM_000037
literal: OntoDM_000037s

AnnotationAssertion
IRI: OntoDM_000037
literal: OntoDM_000037

AnnotationAssertion
IRI: OntoDM_000037
literal: A ensemble of generalizations specification is a generalization specification and denotes a type of generalization that is produced by a ensemble algorithm. This algorithm produces a set of generalizations at its output.

AnnotationAssertion
IRI: OntoDM_000037
literal: ensemble specification

AnnotationAssertion
IRI: OntoDM_000038
literal: OntoDM_000038s

AnnotationAssertion
IRI: OntoDM_000038
literal: OntoDM_000038

AnnotationAssertion
IRI: OntoDM_000038
literal: A data mining algorithm is an algorithm that solves a data mining task and as a results outputs a generalization. It is usually published in some document (journal publication or technical report).

AnnotationAssertion
IRI: OntoDM_000038
literal: data mining algorithm

AnnotationAssertion
IRI: OntoDM_000039
literal: OntoDM_000039

AnnotationAssertion
IRI: OntoDM_000039
literal: OntoDM_000039s

AnnotationAssertion
IRI: OntoDM_000039
literal: OntoDM_000039ed

AnnotationAssertion
IRI: OntoDM_000039
literal: has_data_items_number

AnnotationAssertion
IRI: OntoDM_000041
literal: OntoDM_000041s

AnnotationAssertion
IRI: OntoDM_000041
literal: OntoDM_000041

AnnotationAssertion
IRI: OntoDM_000041
literal: bootstrap sampling process

AnnotationAssertion
IRI: OntoDM_000042
literal: OntoDM_000042s

AnnotationAssertion
IRI: OntoDM_000042
literal: OntoDM_000042

AnnotationAssertion
IRI: OntoDM_000043
literal: OntoDM_000043s

AnnotationAssertion
IRI: OntoDM_000043
literal: OntoDM_000043

AnnotationAssertion
IRI: OntoDM_000043
literal: subsumtion based laguage constraint

AnnotationAssertion
IRI: OntoDM_000044
literal: OntoDM_000044s

AnnotationAssertion
IRI: OntoDM_000044
literal: OntoDM_000044

AnnotationAssertion
IRI: OntoDM_000044
literal: public available resource

AnnotationAssertion
IRI: OntoDM_000047
literal: OntoDM_000047s

AnnotationAssertion
IRI: OntoDM_000047
literal: OntoDM_000047

AnnotationAssertion
IRI: OntoDM_000048
literal: OntoDM_000048s

AnnotationAssertion
IRI: OntoDM_000048
literal: OntoDM_000048

AnnotationAssertion
IRI: OntoDM_000048
literal: generalization interpreter specification

AnnotationAssertion
IRI: OntoDM_000049
literal: OntoDM_000049s

AnnotationAssertion
IRI: OntoDM_000049
literal: OntoDM_000049

AnnotationAssertion
IRI: OntoDM_000050
literal: OntoDM_000050s

AnnotationAssertion
IRI: OntoDM_000050
literal: OntoDM_000050

AnnotationAssertion
IRI: OntoDM_000050
literal: An ensemble of generalizations is a realizable entity that is a concretization of an ensemble specification and it is a specified output of a data mining algorithm execution.

AnnotationAssertion
IRI: OntoDM_000050
literal: ensemble of generalizations

AnnotationAssertion
IRI: OntoDM_000051
literal: OntoDM_000051s

AnnotationAssertion
IRI: OntoDM_000051
literal: OntoDM_000051

AnnotationAssertion
IRI: OntoDM_000051
literal: predictive model quality

AnnotationAssertion
IRI: OntoDM_000052
literal: OntoDM_000052s

AnnotationAssertion
IRI: OntoDM_000052
literal: OntoDM_000052

AnnotationAssertion
IRI: OntoDM_000052
literal: mathematical function specification

AnnotationAssertion
IRI: OntoDM_000053
literal: OntoDM_000053s

AnnotationAssertion
IRI: OntoDM_000053
literal: OntoDM_000053

AnnotationAssertion
IRI: OntoDM_000053
literal: a fold is a dataset that is an output of a cross validation sampling process.

AnnotationAssertion
IRI: OntoDM_000053
literal: fold

AnnotationAssertion
IRI: OntoDM_000055
literal: OntoDM_000055

AnnotationAssertion
IRI: OntoDM_000055
literal: OntoDM_000055s

AnnotationAssertion
IRI: OntoDM_000055
literal: OntoDM_000055ed

AnnotationAssertion
IRI: OntoDM_000055
literal: has_description

AnnotationAssertion
IRI: OntoDM_000059
literal: OntoDM_000059s

AnnotationAssertion
IRI: OntoDM_000059
literal: OntoDM_000059

AnnotationAssertion
IRI: OntoDM_000059
literal: status

AnnotationAssertion
IRI: OntoDM_000063
literal: OntoDM_000063s

AnnotationAssertion
IRI: OntoDM_000063
literal: OntoDM_000063

AnnotationAssertion
IRI: OntoDM_000063
literal: probability distribution quality

AnnotationAssertion
IRI: OntoDM_000068
literal: OntoDM_000068

AnnotationAssertion
IRI: OntoDM_000068
literal: OntoDM_000068s

AnnotationAssertion
IRI: OntoDM_000068
literal: OntoDM_000068ed

AnnotationAssertion
IRI: OntoDM_000068
literal: has_features_number

AnnotationAssertion
IRI: OntoDM_000069
literal: OntoDM_000069s

AnnotationAssertion
IRI: OntoDM_000069
literal: OntoDM_000069

AnnotationAssertion
IRI: OntoDM_000069
literal: optimization evaluation constraint

AnnotationAssertion
IRI: OntoDM_000070
literal: OntoDM_000070s

AnnotationAssertion
IRI: OntoDM_000070
literal: OntoDM_000070

AnnotationAssertion
IRI: OntoDM_000070
literal: An evaluation datum is a data item that is a specified output of generalization evaluation process.

AnnotationAssertion
IRI: OntoDM_000070
literal: evaluation datum

AnnotationAssertion
IRI: OntoDM_000071
literal: OntoDM_000071s

AnnotationAssertion
IRI: OntoDM_000071
literal: OntoDM_000071

AnnotationAssertion
IRI: OntoDM_000071
literal: representation

AnnotationAssertion
IRI: OntoDM_000072
literal: OntoDM_000072s

AnnotationAssertion
IRI: OntoDM_000072
literal: OntoDM_000072

AnnotationAssertion
IRI: OntoDM_000072
literal: Clustering algorithm is a data mining algorithm that solves a clustering task and as a result produces a clustering.

AnnotationAssertion
IRI: OntoDM_000072
literal: clustering algorithm

AnnotationAssertion
IRI: OntoDM_000074
literal: OntoDM_000074

AnnotationAssertion
IRI: OntoDM_000074
literal: OntoDM_000074s

AnnotationAssertion
IRI: OntoDM_000074
literal: OntoDM_000074ed

AnnotationAssertion
IRI: OntoDM_000074
literal: has_name

AnnotationAssertion
IRI: OntoDM_000075
literal: OntoDM_000075s

AnnotationAssertion
IRI: OntoDM_000075
literal: OntoDM_000075

AnnotationAssertion
IRI: OntoDM_000076
literal: OntoDM_000076s

AnnotationAssertion
IRI: OntoDM_000076
literal: OntoDM_000076

AnnotationAssertion
IRI: OntoDM_000076
literal: A single generalization is a realizable entity that is a concretization of a single generalization specification and it is a specified output of a data mining algorithm execution.

AnnotationAssertion
IRI: OntoDM_000076
literal: single generalization

AnnotationAssertion
IRI: OntoDM_000078
literal: OntoDM_000078s

AnnotationAssertion
IRI: OntoDM_000078
literal: OntoDM_000078

AnnotationAssertion
IRI: OntoDM_000078
literal: A patten specification is a single generalization specification and denotes a pattern P on type T which is a boolean funtion on objects of type T.

AnnotationAssertion
IRI: OntoDM_000078
literal: pattern specification

AnnotationAssertion
IRI: OntoDM_000081
literal: OntoDM_000081s

AnnotationAssertion
IRI: OntoDM_000081
literal: OntoDM_000081

AnnotationAssertion
IRI: OntoDM_000081
literal: mean_squared_error

AnnotationAssertion
IRI: OntoDM_000084
literal: OntoDM_000084s

AnnotationAssertion
IRI: OntoDM_000084
literal: OntoDM_000084

AnnotationAssertion
IRI: OntoDM_000084
literal: generalization evaluation

AnnotationAssertion
IRI: OntoDM_000085
literal: OntoDM_000085s

AnnotationAssertion
IRI: OntoDM_000085
literal: OntoDM_000085

AnnotationAssertion
IRI: OntoDM_000085
literal: predictive_modeling_algorithm_implementation

AnnotationAssertion
IRI: OntoDM_000087
literal: OntoDM_000087s

AnnotationAssertion
IRI: OntoDM_000087
literal: OntoDM_000087

AnnotationAssertion
IRI: OntoDM_000087
literal: The task of estimating the joint probability distribution involves constructing a probability disctribution given a set of data of a certain datatype.

AnnotationAssertion
IRI: OntoDM_000087
literal: batch probability distribution estimation task

AnnotationAssertion
IRI: OntoDM_000088
literal: OntoDM_000088

AnnotationAssertion
IRI: OntoDM_000088
literal: OntoDM_000088s

AnnotationAssertion
IRI: OntoDM_000088
literal: OntoDM_000088ed

AnnotationAssertion
IRI: OntoDM_000088
literal: has_number

AnnotationAssertion
IRI: OntoDM_000089
literal: OntoDM_000089s

AnnotationAssertion
IRI: OntoDM_000089
literal: OntoDM_000089

AnnotationAssertion
IRI: OntoDM_000089
literal: language cost function constraint

AnnotationAssertion
IRI: OntoDM_000092
literal: OntoDM_000092s

AnnotationAssertion
IRI: OntoDM_000092
literal: OntoDM_000092

AnnotationAssertion
IRI: OntoDM_000092
literal: The task of pattern discovery is to find all local patterns from a given pattern language that satisfy the required conditions.

AnnotationAssertion
IRI: OntoDM_000092
literal: batch pattern discovery task

AnnotationAssertion
IRI: OntoDM_000095
literal: OntoDM_000095s

AnnotationAssertion
IRI: OntoDM_000095
literal: OntoDM_000095

AnnotationAssertion
IRI: OntoDM_000096
literal: OntoDM_000096s

AnnotationAssertion
IRI: OntoDM_000096
literal: OntoDM_000096

AnnotationAssertion
IRI: OntoDM_000098
literal: OntoDM_000098s

AnnotationAssertion
IRI: OntoDM_000098
literal: OntoDM_000098

AnnotationAssertion
IRI: OntoDM_000098
literal: A data mining task is an objective specification that specifies the objective that a data mining algorithm needs to achieve when executed on a dataset to produce as output a generalization.

AnnotationAssertion
IRI: OntoDM_000098
literal: data mining task

AnnotationAssertion
IRI: OntoDM_000099
literal: OntoDM_000099s

AnnotationAssertion
IRI: OntoDM_000099
literal: OntoDM_000099

AnnotationAssertion
IRI: OntoDM_000099
literal: query specification

AnnotationAssertion
IRI: OntoDM_000100
literal: OntoDM_000100s

AnnotationAssertion
IRI: OntoDM_000100
literal: OntoDM_000100

AnnotationAssertion
IRI: OntoDM_000100
literal: discreteness

AnnotationAssertion
IRI: OntoDM_000101
literal: OntoDM_000101s

AnnotationAssertion
IRI: OntoDM_000101
literal: OntoDM_000101

AnnotationAssertion
IRI: OntoDM_000101
literal: soft language cost function constraint

AnnotationAssertion
IRI: OntoDM_000102
literal: OntoDM_000102s

AnnotationAssertion
IRI: OntoDM_000102
literal: OntoDM_000102

AnnotationAssertion
IRI: OntoDM_000102
literal: probability distribution execution

AnnotationAssertion
IRI: OntoDM_000103
literal: OntoDM_000103s

AnnotationAssertion
IRI: OntoDM_000103
literal: OntoDM_000103

AnnotationAssertion
IRI: OntoDM_000103
literal: hard language cost function constraint

AnnotationAssertion
IRI: OntoDM_000105
literal: OntoDM_000105s

AnnotationAssertion
IRI: OntoDM_000105
literal: OntoDM_000105

AnnotationAssertion
IRI: OntoDM_000105
literal: distance

AnnotationAssertion
IRI: OntoDM_000106
literal: OntoDM_000106s

AnnotationAssertion
IRI: OntoDM_000106
literal: OntoDM_000106

AnnotationAssertion
IRI: OntoDM_000106
literal: Scoring functions often combine evaluation functions and language cost functions.

AnnotationAssertion
IRI: OntoDM_000106
literal: scoring function

AnnotationAssertion
IRI: OntoDM_000107
literal: OntoDM_000107s

AnnotationAssertion
IRI: OntoDM_000107
literal: OntoDM_000107

AnnotationAssertion
IRI: OntoDM_000107
literal: penalty function

AnnotationAssertion
IRI: OntoDM_000109
literal: OntoDM_000109s

AnnotationAssertion
IRI: OntoDM_000109
literal: OntoDM_000109

AnnotationAssertion
IRI: OntoDM_000109
literal: sampling_objective

AnnotationAssertion
IRI: OntoDM_000111
literal: OntoDM_000111s

AnnotationAssertion
IRI: OntoDM_000111
literal: OntoDM_000111

AnnotationAssertion
IRI: OntoDM_000112
literal: OntoDM_000112s

AnnotationAssertion
IRI: OntoDM_000112
literal: OntoDM_000112

AnnotationAssertion
IRI: OntoDM_000112
literal: Clustering specification is a single generalization specification is a generalization specification and denotes a type of generalization that models the mapping of a set of objects S of type T from S to a set of natural numbers {1,...K}. This generalization is obtained by appliing a clustering algorithm on a set of data.

AnnotationAssertion
IRI: OntoDM_000112
literal: clustering specification

AnnotationAssertion
IRI: OntoDM_000114
literal: OntoDM_000114s

AnnotationAssertion
IRI: OntoDM_000114
literal: OntoDM_000114

AnnotationAssertion
IRI: OntoDM_000118
literal: OntoDM_000118s

AnnotationAssertion
IRI: OntoDM_000118
literal: OntoDM_000118

AnnotationAssertion
IRI: OntoDM_000118
literal: Defines properties of a generalization

AnnotationAssertion
IRI: OntoDM_000118
literal: generalization quality

AnnotationAssertion
IRI: OntoDM_000121
literal: OntoDM_000121s

AnnotationAssertion
IRI: OntoDM_000121
literal: OntoDM_000121

AnnotationAssertion
IRI: OntoDM_000121
literal: a bag is a dataset that is an output of a bootstrap sampling process

AnnotationAssertion
IRI: OntoDM_000121
literal: bag

AnnotationAssertion
IRI: OntoDM_000122
literal: OntoDM_000122s

AnnotationAssertion
IRI: OntoDM_000122
literal: OntoDM_000122

AnnotationAssertion
IRI: OntoDM_000122
literal: Function that is used to combine other functions

AnnotationAssertion
IRI: OntoDM_000122
literal: combining function

AnnotationAssertion
IRI: OntoDM_000125
literal: OntoDM_000125s

AnnotationAssertion
IRI: OntoDM_000125
literal: OntoDM_000125

AnnotationAssertion
IRI: OntoDM_000125
literal: integrated_average_squared_error

AnnotationAssertion
IRI: OntoDM_000127
literal: OntoDM_000127s

AnnotationAssertion
IRI: OntoDM_000127
literal: OntoDM_000127

AnnotationAssertion
IRI: OntoDM_000127
literal: clustering algorithm execution

AnnotationAssertion
IRI: OntoDM_000128
literal: OntoDM_000128s

AnnotationAssertion
IRI: OntoDM_000128
literal: OntoDM_000128

AnnotationAssertion
IRI: OntoDM_000128
literal: a feature set specification is a data item specification that specifies a set of features used in a specific data mining task.

AnnotationAssertion
IRI: OntoDM_000128
literal: feature set specification

AnnotationAssertion
IRI: OntoDM_000130
literal: OntoDM_000130s

AnnotationAssertion
IRI: OntoDM_000130
literal: OntoDM_000130

AnnotationAssertion
IRI: OntoDM_000130
literal: A parameter is a quality of an algorithm implementation,and it refers to one of the pieces of data provided as input to the algorithm implementation. The parameter influences the flow of the execution of algorithm realized by a data mining operator that has information about the specific parameter setting used in the execution process .

AnnotationAssertion
IRI: OntoDM_000130
literal: parameter

AnnotationAssertion
IRI: OntoDM_000132
literal: OntoDM_000132s

AnnotationAssertion
IRI: OntoDM_000132
literal: OntoDM_000132

AnnotationAssertion
IRI: OntoDM_000132
literal: A background knowledge is a data item that is input to data mining algorithm along with the data examples, and represents domain information that is essential to proper functioning of an algorithm.

AnnotationAssertion
IRI: OntoDM_000132
literal: Background knowledge can be seen as a set of mappings that generate new features bi:T-->Ti.

AnnotationAssertion
IRI: OntoDM_000132
literal: background knowledge

AnnotationAssertion
IRI: OntoDM_000134
literal: OntoDM_000134s

AnnotationAssertion
IRI: OntoDM_000134
literal: OntoDM_000134

AnnotationAssertion
IRI: OntoDM_000134
literal: clustering_algorithm_implementation

AnnotationAssertion
IRI: OntoDM_000136
literal: OntoDM_000136

AnnotationAssertion
IRI: OntoDM_000136
literal: OntoDM_000136s

AnnotationAssertion
IRI: OntoDM_000136
literal: OntoDM_000136ed

AnnotationAssertion
IRI: OntoDM_000136
literal: has_identifyer

AnnotationAssertion
IRI: OntoDM_000137
literal: OntoDM_000137s

AnnotationAssertion
IRI: OntoDM_000137
literal: OntoDM_000137

AnnotationAssertion
IRI: OntoDM_000137
literal: constraint specification

AnnotationAssertion
IRI: OntoDM_000138
literal: feature-based data example

AnnotationAssertion
IRI: OntoDM_000139
literal: feature-based DM-dataset

AnnotationAssertion
IRI: OntoDM_000140
literal: OntoDM_000140s

AnnotationAssertion
IRI: OntoDM_000140
literal: OntoDM_000140

AnnotationAssertion
IRI: OntoDM_000140
literal: dataset representation

AnnotationAssertion
IRI: OntoDM_000142
literal: OntoDM_000142s

AnnotationAssertion
IRI: OntoDM_000142
literal: OntoDM_000142

AnnotationAssertion
IRI: OntoDM_000142
literal: aggregate function

AnnotationAssertion
IRI: OntoDM_000143
literal: OntoDM_000143s

AnnotationAssertion
IRI: OntoDM_000143
literal: OntoDM_000143

AnnotationAssertion
IRI: OntoDM_000143
literal: dataset sampling

AnnotationAssertion
IRI: OntoDM_000144
literal: OntoDM_000144s

AnnotationAssertion
IRI: OntoDM_000144
literal: OntoDM_000144

AnnotationAssertion
IRI: OntoDM_000144
literal: Dataset is an aggregate of data examples.

AnnotationAssertion
IRI: OntoDM_000144
literal: DM-dataset

AnnotationAssertion
IRI: OntoDM_000145
literal: OntoDM_000145s

AnnotationAssertion
IRI: OntoDM_000145
literal: OntoDM_000145

AnnotationAssertion
IRI: OntoDM_000145
literal: A constant value of some property of a generalization. It is used to define constraints.

AnnotationAssertion
IRI: OntoDM_000145
literal: constraint threshold

AnnotationAssertion
IRI: OntoDM_000149
literal: OntoDM_000149s

AnnotationAssertion
IRI: OntoDM_000149
literal: OntoDM_000149

AnnotationAssertion
IRI: OntoDM_000149
literal: holdout sampling process

AnnotationAssertion
IRI: OntoDM_000155
literal: OntoDM_000155s

AnnotationAssertion
IRI: OntoDM_000155
literal: OntoDM_000155

AnnotationAssertion
IRI: OntoDM_000155
literal: frequency

AnnotationAssertion
IRI: OntoDM_000160
literal: OntoDM_000160s

AnnotationAssertion
IRI: OntoDM_000160
literal: OntoDM_000160

AnnotationAssertion
IRI: OntoDM_000160
literal: Predictive modeling algorithm is a data mining algorithm that solves a predictive modeling task and as a result produces a predictive model.

AnnotationAssertion
IRI: OntoDM_000160
literal: predictive modeling algorithm

AnnotationAssertion
IRI: OntoDM_000162
literal: OntoDM_000162

AnnotationAssertion
IRI: OntoDM_000162
literal: OntoDM_000162s

AnnotationAssertion
IRI: OntoDM_000162
literal: OntoDM_000162ed

AnnotationAssertion
IRI: OntoDM_000162
literal: is-labeled

AnnotationAssertion
IRI: OntoDM_000167
literal: OntoDM_000167s

AnnotationAssertion
IRI: OntoDM_000167
literal: OntoDM_000167

AnnotationAssertion
IRI: OntoDM_000167
literal: pattern set execution

AnnotationAssertion
IRI: OntoDM_000168
literal: OntoDM_000168s

AnnotationAssertion
IRI: OntoDM_000168
literal: OntoDM_000168

AnnotationAssertion
IRI: OntoDM_000168
literal: A data specifcation is-a specifcation entity that specifies on which concrete part of the data the datatype applies to.

AnnotationAssertion
IRI: OntoDM_000168
literal: data specification

AnnotationAssertion
IRI: OntoDM_000170
literal: OntoDM_000170s

AnnotationAssertion
IRI: OntoDM_000170
literal: OntoDM_000170

AnnotationAssertion
IRI: OntoDM_000170
literal: optimization language cost function constraint

AnnotationAssertion
IRI: OntoDM_000172
literal: OntoDM_000172s

AnnotationAssertion
IRI: OntoDM_000172
literal: OntoDM_000172

AnnotationAssertion
IRI: OntoDM_000172
literal: The output data type should be the same as the input datatype because IQ asc as filters This should be put as a axiom or rule in PROLOG

AnnotationAssertion
IRI: OntoDM_000172
literal: inductive query

AnnotationAssertion
IRI: OntoDM_000173
literal: OntoDM_000173s

AnnotationAssertion
IRI: OntoDM_000173
literal: OntoDM_000173

AnnotationAssertion
IRI: OntoDM_000173
literal: The language cost functions are used in language constraints concern the data part of generalizations. Most often they are related to the size/complexity of generalizations.

AnnotationAssertion
IRI: OntoDM_000173
literal: language cost function

AnnotationAssertion
IRI: OntoDM_000175
literal: OntoDM_000175s

AnnotationAssertion
IRI: OntoDM_000175
literal: OntoDM_000175

AnnotationAssertion
IRI: OntoDM_000175
literal: predictive modelling algorithm execution

AnnotationAssertion
IRI: OntoDM_000178
literal: OntoDM_000178s

AnnotationAssertion
IRI: OntoDM_000178
literal: OntoDM_000178

AnnotationAssertion
IRI: OntoDM_000178
literal: A distance function d for type T is a mapping from apirs of objects of type T to non negative reals. It has to satisfy 3 main properties

AnnotationAssertion
IRI: OntoDM_000178
literal: distance function

AnnotationAssertion
IRI: OntoDM_000179
literal: OntoDM_000179s

AnnotationAssertion
IRI: OntoDM_000179
literal: OntoDM_000179

AnnotationAssertion
IRI: OntoDM_000179
literal: An pattern set ensemble is a realizable entity that is a concretization of a pattern set ensemble specification and it is a specified output of a pattern discovery algorithm execution.

AnnotationAssertion
IRI: OntoDM_000179
literal: pattern set ensemble

AnnotationAssertion
IRI: OntoDM_000182
literal: OntoDM_000182s

AnnotationAssertion
IRI: OntoDM_000182
literal: OntoDM_000182

AnnotationAssertion
IRI: OntoDM_000185
literal: OntoDM_000185s

AnnotationAssertion
IRI: OntoDM_000185
literal: OntoDM_000185

AnnotationAssertion
IRI: OntoDM_000185
literal: separate test set evaluation process

AnnotationAssertion
IRI: OntoDM_000191
literal: OntoDM_000191s

AnnotationAssertion
IRI: OntoDM_000191
literal: OntoDM_000191

AnnotationAssertion
IRI: OntoDM_000191
literal: database

AnnotationAssertion
IRI: OntoDM_000192
literal: OntoDM_000192s

AnnotationAssertion
IRI: OntoDM_000192
literal: OntoDM_000192

AnnotationAssertion
IRI: OntoDM_000192
literal: Pattern discovery algorithm is a data mining algorithm that solves a pattern discovery task and as a result produces a set of patterns.

AnnotationAssertion
IRI: OntoDM_000192
literal: pattern discovery algorithm

AnnotationAssertion
IRI: OntoDM_000195
literal: OntoDM_000195s

AnnotationAssertion
IRI: OntoDM_000195
literal: OntoDM_000195

AnnotationAssertion
IRI: OntoDM_000195
literal: training set evaluation process

AnnotationAssertion
IRI: OntoDM_000196
literal: OntoDM_000196s

AnnotationAssertion
IRI: OntoDM_000196
literal: OntoDM_000196

AnnotationAssertion
IRI: OntoDM_000196
literal: algorithm_component_specification

AnnotationAssertion
IRI: OntoDM_000197
literal: OntoDM_000197s

AnnotationAssertion
IRI: OntoDM_000197
literal: OntoDM_000197

AnnotationAssertion
IRI: OntoDM_000197
literal: data creation query

AnnotationAssertion
IRI: OntoDM_000199
literal: OntoDM_000199s

AnnotationAssertion
IRI: OntoDM_000199
literal: OntoDM_000199

AnnotationAssertion
IRI: OntoDM_000199
literal: cost function

AnnotationAssertion
IRI: OntoDM_000200
literal: OntoDM_000200s

AnnotationAssertion
IRI: OntoDM_000200
literal: OntoDM_000200

AnnotationAssertion
IRI: OntoDM_000200
literal: prototype function

AnnotationAssertion
IRI: OntoDM_000201
literal: OntoDM_000201s

AnnotationAssertion
IRI: OntoDM_000201
literal: OntoDM_000201

AnnotationAssertion
IRI: OntoDM_000201
literal: not_discrete

AnnotationAssertion
IRI: OntoDM_000207
literal: OntoDM_000207s

AnnotationAssertion
IRI: OntoDM_000207
literal: OntoDM_000207

AnnotationAssertion
IRI: OntoDM_000207
literal: discrete

AnnotationAssertion
IRI: OntoDM_000209
literal: OntoDM_000209s

AnnotationAssertion
IRI: OntoDM_000209
literal: OntoDM_000209

AnnotationAssertion
IRI: OntoDM_000209
literal: data transformation with generalization query

AnnotationAssertion
IRI: OntoDM_000212
literal: OntoDM_000212s

AnnotationAssertion
IRI: OntoDM_000212
literal: OntoDM_000212

AnnotationAssertion
IRI: OntoDM_000212
literal: language constraint

AnnotationAssertion
IRI: OntoDM_000214
literal: OntoDM_000214s

AnnotationAssertion
IRI: OntoDM_000214
literal: OntoDM_000214

AnnotationAssertion
IRI: OntoDM_000214
literal: draft

AnnotationAssertion
IRI: OntoDM_000219
literal: OntoDM_000219s

AnnotationAssertion
IRI: OntoDM_000219
literal: OntoDM_000219

AnnotationAssertion
IRI: OntoDM_000219
literal: clustering evaluation function specification

AnnotationAssertion
IRI: OntoDM_000223
literal: OntoDM_000223s

AnnotationAssertion
IRI: OntoDM_000223
literal: OntoDM_000223

AnnotationAssertion
IRI: OntoDM_000223
literal: clustering representation

AnnotationAssertion
IRI: OntoDM_000224
literal: OntoDM_000224s

AnnotationAssertion
IRI: OntoDM_000224
literal: OntoDM_000224

AnnotationAssertion
IRI: OntoDM_000224
literal: generalization selection query

AnnotationAssertion
IRI: OntoDM_000228
literal: OntoDM_000228s

AnnotationAssertion
IRI: OntoDM_000228
literal: OntoDM_000228

AnnotationAssertion
IRI: OntoDM_000228
literal: predictive model specification is a single generalization specification and denotes a type of generalization that represents a mapping taking objects of type Td and returns objects of type To.

AnnotationAssertion
IRI: OntoDM_000228
literal: predictive model specification

AnnotationAssertion
IRI: OntoDM_000229
literal: OntoDM_000229s

AnnotationAssertion
IRI: OntoDM_000229
literal: OntoDM_000229

AnnotationAssertion
IRI: OntoDM_000229
literal: data mining algorithm implementation

AnnotationAssertion
IRI: OntoDM_000232
literal: OntoDM_000232s

AnnotationAssertion
IRI: OntoDM_000232
literal: OntoDM_000232

AnnotationAssertion
IRI: OntoDM_000233
literal: OntoDM_000233s

AnnotationAssertion
IRI: OntoDM_000233
literal: OntoDM_000233

AnnotationAssertion
IRI: OntoDM_000233
literal: full fledged query

AnnotationAssertion
IRI: OntoDM_000234
literal: OntoDM_000234s

AnnotationAssertion
IRI: OntoDM_000234
literal: OntoDM_000234

AnnotationAssertion
IRI: OntoDM_000234
literal: clustering execution

AnnotationAssertion
IRI: OntoDM_000235
literal: OntoDM_000235s

AnnotationAssertion
IRI: OntoDM_000235
literal: OntoDM_000235

AnnotationAssertion
IRI: OntoDM_000235
literal: In the task of predictive modeling, we are given a dataset that consists of examples of the form (d,o), where d is of type Td and each o is of type To. To learn a predictive model means to find a mapping from description to output m::Td--->To that fits data closly.

AnnotationAssertion
IRI: OntoDM_000235
literal: predictive modelling task

AnnotationAssertion
IRI: OntoDM_000237
literal: OntoDM_000237s

AnnotationAssertion
IRI: OntoDM_000237
literal: OntoDM_000237

AnnotationAssertion
IRI: OntoDM_000237
literal: soft evaluation constraint

AnnotationAssertion
IRI: OntoDM_000238
literal: OntoDM_000238s

AnnotationAssertion
IRI: OntoDM_000238
literal: OntoDM_000238

AnnotationAssertion
IRI: OntoDM_000239
literal: OntoDM_000239s

AnnotationAssertion
IRI: OntoDM_000239
literal: OntoDM_000239

AnnotationAssertion
IRI: OntoDM_000239
literal: data selection query

AnnotationAssertion
IRI: OntoDM_000243
literal: OntoDM_000243s

AnnotationAssertion
IRI: OntoDM_000243
literal: OntoDM_000243

AnnotationAssertion
IRI: OntoDM_000243
literal: example weight

AnnotationAssertion
IRI: OntoDM_000247
literal: OntoDM_000247s

AnnotationAssertion
IRI: OntoDM_000247
literal: OntoDM_000247

AnnotationAssertion
IRI: OntoDM_000247
literal: Descriptive datatype specification is a data item specification that denotes the datatype of the data from the descriptive part of a dataset.

AnnotationAssertion
IRI: OntoDM_000247
literal: descriptive data specification

AnnotationAssertion
IRI: OntoDM_000249
literal: OntoDM_000249s

AnnotationAssertion
IRI: OntoDM_000249
literal: OntoDM_000249

AnnotationAssertion
IRI: OntoDM_000249
literal: evaluation constraint

AnnotationAssertion
IRI: OntoDM_000250
literal: OntoDM_000250s

AnnotationAssertion
IRI: OntoDM_000250
literal: OntoDM_000250

AnnotationAssertion
IRI: OntoDM_000250
literal: pattern representation

AnnotationAssertion
IRI: OntoDM_000253
literal: OntoDM_000253s

AnnotationAssertion
IRI: OntoDM_000253
literal: OntoDM_000253

AnnotationAssertion
IRI: OntoDM_000253
literal: Probability distribution estimation algorithm is a data mining algorithm that solves a probability distribution estimation task and as a result produces a probability distribution.

AnnotationAssertion
IRI: OntoDM_000253
literal: probability distribution estimation algorithm

AnnotationAssertion
IRI: OntoDM_000255
literal: OntoDM_000255s

AnnotationAssertion
IRI: OntoDM_000255
literal: OntoDM_000255

AnnotationAssertion
IRI: OntoDM_000255
literal: cross validation sampling process

AnnotationAssertion
IRI: OntoDM_000256
literal: OntoDM_000256s

AnnotationAssertion
IRI: OntoDM_000256
literal: OntoDM_000256

AnnotationAssertion
IRI: OntoDM_000256
literal: generalization representation

AnnotationAssertion
IRI: OntoDM_000258
literal: OntoDM_000258s

AnnotationAssertion
IRI: OntoDM_000258
literal: OntoDM_000258

AnnotationAssertion
IRI: OntoDM_000258
literal: background knowledge representation

AnnotationAssertion
IRI: OntoDM_000259
literal: OntoDM_000259s

AnnotationAssertion
IRI: OntoDM_000259
literal: OntoDM_000259

AnnotationAssertion
IRI: OntoDM_000259
literal: pattern discovery evaluation function specification

AnnotationAssertion
IRI: OntoDM_000260
literal: OntoDM_000260s

AnnotationAssertion
IRI: OntoDM_000260
literal: OntoDM_000260

AnnotationAssertion
IRI: OntoDM_000260
literal: N fold validation evaluation process

AnnotationAssertion
IRI: OntoDM_000261
literal: OntoDM_000261s

AnnotationAssertion
IRI: OntoDM_000261
literal: OntoDM_000261

AnnotationAssertion
IRI: OntoDM_000261
literal: knowledge discovery scenario objective

AnnotationAssertion
IRI: OntoDM_000264
literal: OntoDM_000264s

AnnotationAssertion
IRI: OntoDM_000264
literal: OntoDM_000264

AnnotationAssertion
IRI: OntoDM_000264
literal: The task of clustering in general is concerned with grouping objects into classes of similar objects. Given a set of examples (object descriptions), the task of clustering is to partition these examples into subsets, called clusters.

AnnotationAssertion
IRI: OntoDM_000264
literal: batch clustering task

AnnotationAssertion
IRI: OntoDM_000265
literal: OntoDM_000265s

AnnotationAssertion
IRI: OntoDM_000265
literal: OntoDM_000265

AnnotationAssertion
IRI: OntoDM_000265
literal: probability_distribution_estimation_algorithm_implementation

AnnotationAssertion
IRI: OntoDM_000266
literal: OntoDM_000266s

AnnotationAssertion
IRI: OntoDM_000266
literal: OntoDM_000266

AnnotationAssertion
IRI: OntoDM_000266
literal: selection composition of generalizations query

AnnotationAssertion
IRI: OntoDM_000269
literal: OntoDM_000269s

AnnotationAssertion
IRI: OntoDM_000269
literal: OntoDM_000269

AnnotationAssertion
IRI: OntoDM_000269
literal: train set role

AnnotationAssertion
IRI: OntoDM_000270
literal: OntoDM_000270s

AnnotationAssertion
IRI: OntoDM_000270
literal: OntoDM_000270

AnnotationAssertion
IRI: OntoDM_000270
literal: primitive constraint

AnnotationAssertion
IRI: OntoDM_000271
literal: OntoDM_000271s

AnnotationAssertion
IRI: OntoDM_000271
literal: OntoDM_000271

AnnotationAssertion
IRI: OntoDM_000271
literal: probability distribution evaluation function specification

AnnotationAssertion
IRI: OntoDM_000273
literal: OntoDM_000273s

AnnotationAssertion
IRI: OntoDM_000273
literal: OntoDM_000273

AnnotationAssertion
IRI: OntoDM_000273
literal: generalizations and data from generalizations query

AnnotationAssertion
IRI: OntoDM_000276
literal: OntoDM_000276s

AnnotationAssertion
IRI: OntoDM_000276
literal: OntoDM_000276

AnnotationAssertion
IRI: OntoDM_000276
literal: hard evaluation constraint

AnnotationAssertion
IRI: OntoDM_000280
literal: OntoDM_000280s

AnnotationAssertion
IRI: OntoDM_000280
literal: OntoDM_000280

AnnotationAssertion
IRI: OntoDM_000280
literal: predictive model representation

AnnotationAssertion
IRI: OntoDM_000282
literal: OntoDM_000282s

AnnotationAssertion
IRI: OntoDM_000282
literal: OntoDM_000282

AnnotationAssertion
IRI: OntoDM_000282
literal: predictive model evaluation

AnnotationAssertion
IRI: OntoDM_000285
literal: OntoDM_000285s

AnnotationAssertion
IRI: OntoDM_000285
literal: OntoDM_000285

AnnotationAssertion
IRI: OntoDM_000285
literal: A probability distribution specification is a single generalization specification and denotes a type of generalization that is a mapping from objects of type T to non-negative reals. This generalization is an output of a probability distribution estimation algorithm.

AnnotationAssertion
IRI: OntoDM_000285
literal: probability distribution specification

AnnotationAssertion
IRI: OntoDM_000287
literal: OntoDM_000287s

AnnotationAssertion
IRI: OntoDM_000287
literal: OntoDM_000287

AnnotationAssertion
IRI: OntoDM_000287
literal: pattern discovery algorithm execution

AnnotationAssertion
IRI: OntoDM_000288
literal: OntoDM_000288s

AnnotationAssertion
IRI: OntoDM_000288
literal: OntoDM_000288

AnnotationAssertion
IRI: OntoDM_000288
literal: pattern_discovery_algorithm_implementation

AnnotationAssertion
IRI: OntoDM_000290
literal: OntoDM_000290s

AnnotationAssertion
IRI: OntoDM_000290
literal: OntoDM_000290

AnnotationAssertion
IRI: OntoDM_000291
literal: OntoDM_000291s

AnnotationAssertion
IRI: OntoDM_000291
literal: OntoDM_000291

AnnotationAssertion
IRI: OntoDM_000291
literal: refinement operator

AnnotationAssertion
IRI: OntoDM_000293
literal: OntoDM_000293s

AnnotationAssertion
IRI: OntoDM_000293
literal: OntoDM_000293

AnnotationAssertion
IRI: OntoDM_000293
literal: intra_cluster_variance

AnnotationAssertion
IRI: OntoDM_000294
literal: OntoDM_000294s

AnnotationAssertion
IRI: OntoDM_000294
literal: OntoDM_000294

AnnotationAssertion
IRI: OntoDM_000294
literal: probability_distribution_scoring_function

AnnotationAssertion
IRI: OntoDM_000297
literal: OntoDM_000297s

AnnotationAssertion
IRI: OntoDM_000297
literal: OntoDM_000297

AnnotationAssertion
IRI: OntoDM_000297
literal: generalization execution

AnnotationAssertion
IRI: OntoDM_000298
literal: OntoDM_000298s

AnnotationAssertion
IRI: OntoDM_000298
literal: OntoDM_000298

AnnotationAssertion
IRI: OntoDM_000298
literal: A feature specification is a data item specification that specifies a primitive data feature (belonging to a primitive datatype), used in propositional data mining where the descriptive datatype is usually a tuple of primitive features.

AnnotationAssertion
IRI: OntoDM_000298
literal: feature specification

AnnotationAssertion
IRI: OntoDM_000299
literal: OntoDM_000299s

AnnotationAssertion
IRI: OntoDM_000299
literal: OntoDM_000299

AnnotationAssertion
IRI: OntoDM_000300
literal: OntoDM_000300s

AnnotationAssertion
IRI: OntoDM_000300
literal: OntoDM_000300

AnnotationAssertion
IRI: OntoDM_000300
literal: generalizations and data from data query

AnnotationAssertion
IRI: OntoDM_000302
literal: OntoDM_000302s

AnnotationAssertion
IRI: OntoDM_000302
literal: OntoDM_000302

AnnotationAssertion
IRI: OntoDM_000302
literal: optimization function

AnnotationAssertion
IRI: OntoDM_000306
literal: OntoDM_000306s

AnnotationAssertion
IRI: OntoDM_000306
literal: OntoDM_000306

AnnotationAssertion
IRI: OntoDM_000306
literal: probability distribution estimation algorithm execution

AnnotationAssertion
IRI: OntoDM_000309
literal: OntoDM_000309s

AnnotationAssertion
IRI: OntoDM_000309
literal: OntoDM_000309

AnnotationAssertion
IRI: OntoDM_000309
literal: predictive model execution

AnnotationAssertion
IRI: OntoDM_000311
literal: OntoDM_000311

AnnotationAssertion
IRI: OntoDM_000311
literal: OntoDM_000311s

AnnotationAssertion
IRI: OntoDM_000311
literal: OntoDM_000311ed

AnnotationAssertion
IRI: OntoDM_000311
literal: has_URL

AnnotationAssertion
IRI: OntoDM_000316
literal: OntoDM_000316s

AnnotationAssertion
IRI: OntoDM_000316
literal: OntoDM_000316

AnnotationAssertion
IRI: OntoDM_000316
literal: A clustering C on a set of objects S of type T is a function from S to {1,...,k} where k is the number of clusters.

AnnotationAssertion
IRI: OntoDM_000316
literal: clustering

AnnotationAssertion
IRI: OntoDM_000333
literal: OntoDM_000333s

AnnotationAssertion
IRI: OntoDM_000333
literal: OntoDM_000333

AnnotationAssertion
IRI: OntoDM_000333
literal: itemized list

AnnotationAssertion
IRI: OntoDM_000343
literal: OntoDM_000343s

AnnotationAssertion
IRI: OntoDM_000343
literal: OntoDM_000343

AnnotationAssertion
IRI: OntoDM_000343
literal: A generalization is a realizable entity that is a concretization of a generalization specification and it is a specified output of a data mining algorithm execution.

AnnotationAssertion
IRI: OntoDM_000343
literal: generalization

AnnotationAssertion
IRI: OntoDM_001019
literal: OntoDM_001019s

AnnotationAssertion
IRI: OntoDM_001019
literal: OntoDM_001019

AnnotationAssertion
IRI: OntoDM_001019
literal: tuple output specification

AnnotationAssertion
IRI: OntoDM_001020
literal: OntoDM_001020s

AnnotationAssertion
IRI: OntoDM_001020
literal: OntoDM_001020

AnnotationAssertion
IRI: OntoDM_001020
literal: tuple of booleanORdiscrete output specification

AnnotationAssertion
IRI: OntoDM_001021
literal: OntoDM_001021s

AnnotationAssertion
IRI: OntoDM_001021
literal: OntoDM_001021

AnnotationAssertion
IRI: OntoDM_001021
literal: set output specification output specification

AnnotationAssertion
IRI: OntoDM_001022
literal: OntoDM_001022s

AnnotationAssertion
IRI: OntoDM_001022
literal: OntoDM_001022

AnnotationAssertion
IRI: OntoDM_001022
literal: booleanORdiscrete output data specification

AnnotationAssertion
IRI: OntoDM_001334
literal: OntoDM_001334s

AnnotationAssertion
IRI: OntoDM_001334
literal: OntoDM_001334

AnnotationAssertion
IRI: OntoDM_001334
literal: Regression algorithm is a data mining algorithm that solves a regression task and as a result produces a regression model.

AnnotationAssertion
IRI: OntoDM_001334
literal: regression algorithm

AnnotationAssertion
IRI: OntoDM_003932
literal: OntoDM_003932s

AnnotationAssertion
IRI: OntoDM_003932
literal: OntoDM_003932

AnnotationAssertion
IRI: OntoDM_003932
literal: A predictive model for primitive output is a predictive model specification and denotes predictive models that are built on data having primitive datatype on the output part.

AnnotationAssertion
IRI: OntoDM_003932
literal: predictive model for primitive output

AnnotationAssertion
IRI: OntoDM_004843
literal: OntoDM_004843s

AnnotationAssertion
IRI: OntoDM_004843
literal: OntoDM_004843

AnnotationAssertion
IRI: OntoDM_004843
literal: dataset role

AnnotationAssertion
IRI: OntoDM_020420
literal: OntoDM_020420s

AnnotationAssertion
IRI: OntoDM_020420
literal: OntoDM_020420

AnnotationAssertion
IRI: OntoDM_020420
literal: LCPN DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_020420
literal: LCPN DAG based HC algorithm

AnnotationAssertion
IRI: OntoDM_036834
literal: OntoDM_036834s

AnnotationAssertion
IRI: OntoDM_036834
literal: OntoDM_036834

AnnotationAssertion
IRI: OntoDM_036834
literal: LCPN tree-based hierarchical classification algorithm for multiple path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_036834
literal: LPCN tree based HC algorithm for multiple path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_038162
literal: OntoDM_038162s

AnnotationAssertion
IRI: OntoDM_038162
literal: OntoDM_038162

AnnotationAssertion
IRI: OntoDM_038162
literal: data mining operator

AnnotationAssertion
IRI: OntoDM_039503
literal: OntoDM_039503s

AnnotationAssertion
IRI: OntoDM_039503
literal: OntoDM_039503

AnnotationAssertion
IRI: OntoDM_039503
literal: A binary classification model is a classification model and denotes predictive models that are built on data having a boolean datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_039503
literal: binary classification model

AnnotationAssertion
IRI: OntoDM_056700
literal: OntoDM_056700s

AnnotationAssertion
IRI: OntoDM_056700
literal: OntoDM_056700

AnnotationAssertion
IRI: OntoDM_056700
literal: DAG based hierarchical classification task with multiple label paths and full depth labeling is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels and all instances have full depth of labeling.

AnnotationAssertion
IRI: OntoDM_056700
literal: DAG based hierarchical classification task with multiple label paths and full depth labeling

AnnotationAssertion
IRI: OntoDM_057491
literal: OntoDM_057491s

AnnotationAssertion
IRI: OntoDM_057491
literal: OntoDM_057491

AnnotationAssertion
IRI: OntoDM_057491
literal: DAG based hierarchical classification task with single label paths is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have single paths of labels.

AnnotationAssertion
IRI: OntoDM_057491
literal: DAG based hierarchical classification task with single label paths

AnnotationAssertion
IRI: OntoDM_063973
literal: OntoDM_063973s

AnnotationAssertion
IRI: OntoDM_063973
literal: OntoDM_063973

AnnotationAssertion
IRI: OntoDM_063973
literal: DAG-based hierarchical classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a labeled DAG datatype with boolean edges and discrete nodes on the output part of the data.

AnnotationAssertion
IRI: OntoDM_063973
literal: DAG-based hierarchical classification dataset

AnnotationAssertion
IRI: OntoDM_067028
literal: OntoDM_067028s

AnnotationAssertion
IRI: OntoDM_067028
literal: OntoDM_067028

AnnotationAssertion
IRI: OntoDM_067028
literal: Multi-target binary classification model is a multi-target classification model and denotes predictive models that are built on data having a tuple of boolean (set of output features) on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_067028
literal: multi-target binary classification model

AnnotationAssertion
IRI: OntoDM_071450
literal: OntoDM_071450s

AnnotationAssertion
IRI: OntoDM_071450
literal: OntoDM_071450

AnnotationAssertion
IRI: OntoDM_071450
literal: LCL DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_071450
literal: LCL DAG based HC algorithm

AnnotationAssertion
IRI: OntoDM_078701
literal: OntoDM_078701s

AnnotationAssertion
IRI: OntoDM_078701
literal: OntoDM_078701

AnnotationAssertion
IRI: OntoDM_078701
literal: transactional dataset is a dataset specification of unlabeled dataset with a set of discrete datatype on the descriptive part of the data.

AnnotationAssertion
IRI: OntoDM_078701
literal: transactional dataset

AnnotationAssertion
IRI: OntoDM_081165
literal: OntoDM_081165s

AnnotationAssertion
IRI: OntoDM_081165
literal: OntoDM_081165

AnnotationAssertion
IRI: OntoDM_081165
literal: LCN tree-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_081165
literal: LCN tree based HC algorithm for multiple path prediction

AnnotationAssertion
IRI: OntoDM_086891
literal: OntoDM_086891s

AnnotationAssertion
IRI: OntoDM_086891
literal: OntoDM_086891

AnnotationAssertion
IRI: OntoDM_086891
literal: LCL DAG-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_086891
literal: LCL DAG based HC algorithm for single path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_099826
literal: OntoDM_099826s

AnnotationAssertion
IRI: OntoDM_099826
literal: OntoDM_099826

AnnotationAssertion
IRI: OntoDM_099826
literal: predictive models ensemble specification is a ensemble specification and denotes a type of generalization that is produced by a predictive model ensemble algorithm. This algorithm produces a collection of predictive models at its output.

AnnotationAssertion
IRI: OntoDM_099826
literal: predictive models ensemble specification

AnnotationAssertion
IRI: OntoDM_10000000135
literal: data stream

AnnotationAssertion
IRI: OntoDM_10000000136
literal: data stream example

AnnotationAssertion
IRI: OntoDM_10000000137
literal: data stream representation

AnnotationAssertion
IRI: OntoDM_10000000138
literal: data stream quality

AnnotationAssertion
IRI: OntoDM_101020
literal: OntoDM_101020s

AnnotationAssertion
IRI: OntoDM_101020
literal: OntoDM_101020

AnnotationAssertion
IRI: OntoDM_101020
literal: tag

AnnotationAssertion
IRI: OntoDM_104594
literal: OntoDM_104594s

AnnotationAssertion
IRI: OntoDM_104594
literal: OntoDM_104594

AnnotationAssertion
IRI: OntoDM_104594
literal: LCN DAG-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_104594
literal: LCN DAG based HC algorithm for single path prediction

AnnotationAssertion
IRI: OntoDM_107836
literal: OntoDM_107836s

AnnotationAssertion
IRI: OntoDM_107836
literal: OntoDM_107836

AnnotationAssertion
IRI: OntoDM_107836
literal: Hierarchical classification model is afeature-based predictive model for structured output and denotes predictive models that are built on data having a labeled graph datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_107836
literal: hierarchical classification model

AnnotationAssertion
IRI: OntoDM_108244
literal: OntoDM_108244s

AnnotationAssertion
IRI: OntoDM_108244
literal: OntoDM_108244

AnnotationAssertion
IRI: OntoDM_108244
literal: Multi-class classification is a data mining algorithm that solves a multi-class classification task and as a result produces a multi-class classification model.

AnnotationAssertion
IRI: OntoDM_108244
literal: multi-class classification algorithm

AnnotationAssertion
IRI: OntoDM_108452
literal: OntoDM_108452s

AnnotationAssertion
IRI: OntoDM_108452
literal: OntoDM_108452

AnnotationAssertion
IRI: OntoDM_108452
literal: GC DAG based HC algorithm for multiple path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_112569
literal: OntoDM_112569s

AnnotationAssertion
IRI: OntoDM_112569
literal: OntoDM_112569

AnnotationAssertion
IRI: OntoDM_112569
literal: algorithm implementation

AnnotationAssertion
IRI: OntoDM_112772
literal: OntoDM_112772s

AnnotationAssertion
IRI: OntoDM_112772
literal: OntoDM_112772

AnnotationAssertion
IRI: OntoDM_112772
literal: A classification model is a feature-based predictive model for primitive output and denotes predictive models that are built on data having boolean or discrete datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_112772
literal: classification model

AnnotationAssertion
IRI: OntoDM_114697
literal: OntoDM_114697s

AnnotationAssertion
IRI: OntoDM_114697
literal: OntoDM_114697

AnnotationAssertion
IRI: OntoDM_114697
literal: Multi-target prediction task is a feature-based structured output prediction task where the objective is to predict a set of features (represented with a output tuple of primitives datatype) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_114697
literal: supervised multi-target prediction task

AnnotationAssertion
IRI: OntoDM_135761
literal: OntoDM_135761s

AnnotationAssertion
IRI: OntoDM_135761
literal: OntoDM_135761

AnnotationAssertion
IRI: OntoDM_135761
literal: GC DAG-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a global classifier (GC) approach.

AnnotationAssertion
IRI: OntoDM_135761
literal: GC DAG based HC algorithm for single path prediction

AnnotationAssertion
IRI: OntoDM_136533
literal: OntoDM_136533s

AnnotationAssertion
IRI: OntoDM_136533
literal: OntoDM_136533

AnnotationAssertion
IRI: OntoDM_136533
literal: Multi-target regression tree is a multi-target regression model and denotes predictive models that are built on data having a tuple of reals (set of output features) on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and is expressed in the language of decision trees.

AnnotationAssertion
IRI: OntoDM_136533
literal: multi-target regression tree

AnnotationAssertion
IRI: OntoDM_139240
literal: OntoDM_139240s

AnnotationAssertion
IRI: OntoDM_139240
literal: OntoDM_139240

AnnotationAssertion
IRI: OntoDM_139240
literal: scenario title

AnnotationAssertion
IRI: OntoDM_139258
literal: OntoDM_139258s

AnnotationAssertion
IRI: OntoDM_139258
literal: OntoDM_139258

AnnotationAssertion
IRI: OntoDM_139258
literal: Multi-label decision tree is a multi-label classification model and denotes predictive models that are built on data having a set of discrete datatype on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and it is expressed in the language of decision trees.

AnnotationAssertion
IRI: OntoDM_139258
literal: multi-label decision tree

AnnotationAssertion
IRI: OntoDM_145996
literal: OntoDM_145996s

AnnotationAssertion
IRI: OntoDM_145996
literal: OntoDM_145996

AnnotationAssertion
IRI: OntoDM_145996
literal: output tuple of boolean specification is an output tuple of primitives specification that denotes a tuple of boolean datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_145996
literal: output:tuple of boolean

AnnotationAssertion
IRI: OntoDM_147247
literal: OntoDM_147247s

AnnotationAssertion
IRI: OntoDM_147247
literal: OntoDM_147247

AnnotationAssertion
IRI: OntoDM_147247
literal: algorithm execution

AnnotationAssertion
IRI: OntoDM_150825
literal: OntoDM_150825s

AnnotationAssertion
IRI: OntoDM_150825
literal: OntoDM_150825

AnnotationAssertion
IRI: OntoDM_150825
literal: output discrete specification is an output primitive datatype specification that denotes a discrete datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_150825
literal: output:discrete

AnnotationAssertion
IRI: OntoDM_153542
literal: OntoDM_153542s

AnnotationAssertion
IRI: OntoDM_153542
literal: OntoDM_153542

AnnotationAssertion
IRI: OntoDM_153542
literal: output set of reals specification is an output set specification that denotes a set of reals datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_153542
literal: output:set of reals

AnnotationAssertion
IRI: OntoDM_155699
literal: OntoDM_155699s

AnnotationAssertion
IRI: OntoDM_155699
literal: OntoDM_155699

AnnotationAssertion
IRI: OntoDM_155699
literal: Binary classification is a data mining algorithm that solves a binary classification task and as a result produces a binary classification model.

AnnotationAssertion
IRI: OntoDM_155699
literal: binary classification algorithm

AnnotationAssertion
IRI: OntoDM_157687
literal: OntoDM_157687s

AnnotationAssertion
IRI: OntoDM_157687
literal: OntoDM_157687

AnnotationAssertion
IRI: OntoDM_157687
literal: A structure-based predictive model for structured output is a predictive model specification and denotes predictive models that are built on data having structured datatype on the output part, and a structured datatype on the descriptive part.

AnnotationAssertion
IRI: OntoDM_157687
literal: structure-based predictive model for structured output

AnnotationAssertion
IRI: OntoDM_165764
literal: OntoDM_165764s

AnnotationAssertion
IRI: OntoDM_165764
literal: OntoDM_165764

AnnotationAssertion
IRI: OntoDM_165764
literal: A feature-based predictive model for structured output is a predictive model specification and denotes predictive models that are built on data having a structured datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_165764
literal: feature-based predictive model for structured output

AnnotationAssertion
IRI: OntoDM_169891
literal: OntoDM_169891s

AnnotationAssertion
IRI: OntoDM_169891
literal: OntoDM_169891

AnnotationAssertion
IRI: OntoDM_169891
literal: unlabeled structure-based dataset is a dataset specification of unlabeled dataset with a structured descriptive datatype specification.

AnnotationAssertion
IRI: OntoDM_169891
literal: structure-based unlabeled dataset

AnnotationAssertion
IRI: OntoDM_171335
literal: OntoDM_171335

AnnotationAssertion
IRI: OntoDM_171335
literal: C

AnnotationAssertion
IRI: OntoDM_171503
literal: OntoDM_171503s

AnnotationAssertion
IRI: OntoDM_171503
literal: OntoDM_171503

AnnotationAssertion
IRI: OntoDM_171503
literal: Flat classification algorithm is a data mining algorithm that solves a classification task and as a result produces a classification model.

AnnotationAssertion
IRI: OntoDM_171503
literal: flat classification algorithm

AnnotationAssertion
IRI: OntoDM_174112
literal: OntoDM_174112s

AnnotationAssertion
IRI: OntoDM_174112
literal: OntoDM_174112

AnnotationAssertion
IRI: OntoDM_174112
literal: LCPN tree-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_174112
literal: LPCN tree based HC algorithm for multiple path prediction

AnnotationAssertion
IRI: OntoDM_180345
literal: OntoDM_180345s

AnnotationAssertion
IRI: OntoDM_180345
literal: OntoDM_180345

AnnotationAssertion
IRI: OntoDM_180345
literal: LCN DAG-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_180345
literal: LCN DAG based HC algorithm for multiple path prediction

AnnotationAssertion
IRI: OntoDM_182357
literal: OntoDM_182357s

AnnotationAssertion
IRI: OntoDM_182357
literal: OntoDM_182357

AnnotationAssertion
IRI: OntoDM_182357
literal: data mining scenario

AnnotationAssertion
IRI: OntoDM_191108
literal: OntoDM_191108s

AnnotationAssertion
IRI: OntoDM_191108
literal: OntoDM_191108

AnnotationAssertion
IRI: OntoDM_191108
literal: output real specification is an output primitive datatype specification that denotes a real datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_191108
literal: output:real

AnnotationAssertion
IRI: OntoDM_201436
literal: OntoDM_201436s

AnnotationAssertion
IRI: OntoDM_201436
literal: OntoDM_201436

AnnotationAssertion
IRI: OntoDM_201436
literal: parameter specification is a specification entity that is about a parameter.

AnnotationAssertion
IRI: OntoDM_201436
literal: parameter specification

AnnotationAssertion
IRI: OntoDM_205203
literal: OntoDM_205203s

AnnotationAssertion
IRI: OntoDM_205203
literal: OntoDM_205203

AnnotationAssertion
IRI: OntoDM_205203
literal: Feature-based primitive output prediction task is a predicting primitive output task where the objective is to predict a primitive output (represented with a primitive datatype) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_205203
literal: supervised feature-based primitive output prediction task

AnnotationAssertion
IRI: OntoDM_206736
literal: OntoDM_206736

AnnotationAssertion
IRI: OntoDM_206736
literal: JAVA

AnnotationAssertion
IRI: OntoDM_206736
literal: OntoDM_206736

AnnotationAssertion
IRI: OntoDM_207566
literal: OntoDM_207566s

AnnotationAssertion
IRI: OntoDM_207566
literal: OntoDM_207566

AnnotationAssertion
IRI: OntoDM_207566
literal: A feature-based predictive model for primitive output is a predictive model specification and denotes predictive models that are built on data having primitive datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_207566
literal: feature-based predictive model for primitive output

AnnotationAssertion
IRI: OntoDM_208012
literal: OntoDM_208012s

AnnotationAssertion
IRI: OntoDM_208012
literal: OntoDM_208012

AnnotationAssertion
IRI: OntoDM_208012
literal: Time-series prediction task is a feature-based structured output prediction task where the objective is to predict a time series (represented with a output sequence of real datatype) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_208012
literal: supervised time-series prediction task

AnnotationAssertion
IRI: OntoDM_208659
literal: OntoDM_208659s

AnnotationAssertion
IRI: OntoDM_208659
literal: OntoDM_208659

AnnotationAssertion
IRI: OntoDM_208659
literal: binary classification task is a flat classification task where the objective is to predict a boolean output (represented with a boolean datatype) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_208659
literal: supervised binary classification task

AnnotationAssertion
IRI: OntoDM_213136
literal: OntoDM_213136s

AnnotationAssertion
IRI: OntoDM_213136
literal: OntoDM_213136

AnnotationAssertion
IRI: OntoDM_213136
literal: GC DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model using a global classifier (GC) approach.

AnnotationAssertion
IRI: OntoDM_213136
literal: GC DAG based HC algorithm

AnnotationAssertion
IRI: OntoDM_221045
literal: OntoDM_221045s

AnnotationAssertion
IRI: OntoDM_221045
literal: OntoDM_221045

AnnotationAssertion
IRI: OntoDM_221045
literal: LCN DAG-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_221045
literal: LCPN DAG based HC algorithm for single path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_225681
literal: OntoDM_225681s

AnnotationAssertion
IRI: OntoDM_225681
literal: OntoDM_225681

AnnotationAssertion
IRI: OntoDM_225681
literal: Multi-target prediction model is afeature-based predictive model for structured output and denotes predictive models that are built on data having a tuple of primitives (set of output features) on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_225681
literal: multi-target prediction model

AnnotationAssertion
IRI: OntoDM_231548
literal: OntoDM_231548s

AnnotationAssertion
IRI: OntoDM_231548
literal: OntoDM_231548

AnnotationAssertion
IRI: OntoDM_231548
literal: Multi-target binary classification task is a multi-target classification task where the objective is to predict a set of binary features (represented with a output tuple of booleans ) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_231548
literal: supervised multi-target binary classification

AnnotationAssertion
IRI: OntoDM_232231
literal: OntoDM_232231s

AnnotationAssertion
IRI: OntoDM_232231
literal: OntoDM_232231

AnnotationAssertion
IRI: OntoDM_232231
literal: LCPN DAG-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_232231
literal: LCPN DAG based HC algorithm for multiple path prediction

AnnotationAssertion
IRI: OntoDM_242219
literal: OntoDM_242219s

AnnotationAssertion
IRI: OntoDM_242219
literal: OntoDM_242219

AnnotationAssertion
IRI: OntoDM_242219
literal: predicting primitive output task is a predictive modeling task where the objective is to predict a primitive output (represented with a primitive datatype).

AnnotationAssertion
IRI: OntoDM_242219
literal: supervised primitive output prediction task

AnnotationAssertion
IRI: OntoDM_246002
literal: OntoDM_246002s

AnnotationAssertion
IRI: OntoDM_246002
literal: OntoDM_246002

AnnotationAssertion
IRI: OntoDM_246002
literal: descriptive structured data specification is a descriptive datatype specification that denotes a structured datatype on the descriptive part of the data.

AnnotationAssertion
IRI: OntoDM_246002
literal: structure-based descriptive data specification

AnnotationAssertion
IRI: OntoDM_246907
literal: OntoDM_246907s

AnnotationAssertion
IRI: OntoDM_246907
literal: OntoDM_246907

AnnotationAssertion
IRI: OntoDM_246907
literal: Tree based hierarchical classification task with single label paths and partial depth labeling is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have single paths of labels and at least one instance has a partial depth of labeling.

AnnotationAssertion
IRI: OntoDM_246907
literal: tree-based hierarchical classification task with single label paths and partial depth labeling

AnnotationAssertion
IRI: OntoDM_247241
literal: OntoDM_247241s

AnnotationAssertion
IRI: OntoDM_247241
literal: OntoDM_247241

AnnotationAssertion
IRI: OntoDM_247241
literal: Multi-label classification model is afeature-based predictive model for structured output and denotes predictive models that are built on data having a set of discrete datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_247241
literal: multi-label classification model

AnnotationAssertion
IRI: OntoDM_247510
literal: OntoDM_247510s

AnnotationAssertion
IRI: OntoDM_247510
literal: OntoDM_247510

AnnotationAssertion
IRI: OntoDM_247510
literal: output set specification is an output structured datatype specification that denotes a set datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_247510
literal: output:set of primitives

AnnotationAssertion
IRI: OntoDM_251508
literal: OntoDM_251508s

AnnotationAssertion
IRI: OntoDM_251508
literal: OntoDM_251508

AnnotationAssertion
IRI: OntoDM_251508
literal: LCPN tree-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_251508
literal: LPCN tree based HC algorithm for single path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_257039
literal: OntoDM_257039s

AnnotationAssertion
IRI: OntoDM_257039
literal: OntoDM_257039

AnnotationAssertion
IRI: OntoDM_257039
literal: flat classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a boolean or discrete datatype on the output part of the data (specified with the feature specification).

AnnotationAssertion
IRI: OntoDM_257039
literal: flat classification dataset

AnnotationAssertion
IRI: OntoDM_257366
literal: OntoDM_257366s

AnnotationAssertion
IRI: OntoDM_257366
literal: OntoDM_257366

AnnotationAssertion
IRI: OntoDM_257366
literal: LCN tree-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_257366
literal: LCN tree based HC algorithm for single path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_278835
literal: OntoDM_278835s

AnnotationAssertion
IRI: OntoDM_278835
literal: OntoDM_278835

AnnotationAssertion
IRI: OntoDM_278835
literal: output sequence of discrete specification is an output sequence datatype specification that denotes a sequence of discrete datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_278835
literal: output:sequence of discrete

AnnotationAssertion
IRI: OntoDM_279492
literal: OntoDM_279492s

AnnotationAssertion
IRI: OntoDM_279492
literal: OntoDM_279492

AnnotationAssertion
IRI: OntoDM_279492
literal: output DAG specification is an output labeled graph specification that denotes a DAG datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_279492
literal: output:DAG

AnnotationAssertion
IRI: OntoDM_279507
literal: OntoDM_279507s

AnnotationAssertion
IRI: OntoDM_279507
literal: OntoDM_279507

AnnotationAssertion
IRI: OntoDM_280661
literal: OntoDM_280661s

AnnotationAssertion
IRI: OntoDM_280661
literal: OntoDM_280661

AnnotationAssertion
IRI: OntoDM_280661
literal: A regression tree is a regression model and denotes predictive models that are built on data having a real datatype on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and it is expressed in the language of decision trees.

AnnotationAssertion
IRI: OntoDM_280661
literal: regression tree

AnnotationAssertion
IRI: OntoDM_281640
literal: OntoDM_281640s

AnnotationAssertion
IRI: OntoDM_281640
literal: OntoDM_281640

AnnotationAssertion
IRI: OntoDM_281640
literal: DAG based hierarchical classification task with single label paths and partial depth labeling is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have single paths of labels and at least one instance has a partial depth of labeling.

AnnotationAssertion
IRI: OntoDM_281640
literal: DAG based hierarchical classification task with single label paths and partial depth labeling

AnnotationAssertion
IRI: OntoDM_285570
literal: OntoDM_285570s

AnnotationAssertion
IRI: OntoDM_285570
literal: OntoDM_285570

AnnotationAssertion
IRI: OntoDM_285570
literal: workflow execution

AnnotationAssertion
IRI: OntoDM_295724
literal: OntoDM_295724s

AnnotationAssertion
IRI: OntoDM_295724
literal: OntoDM_295724

AnnotationAssertion
IRI: OntoDM_295724
literal: Structure-based predictive modeling algorithm for structured output is a data mining algorithm that solves a structure-based structured output prediction task and as a result produces a structure-based predictive model for structured output.

AnnotationAssertion
IRI: OntoDM_295724
literal: structure-based predictive modeling algorithm for structured output

AnnotationAssertion
IRI: OntoDM_300937
literal: OntoDM_300937s

AnnotationAssertion
IRI: OntoDM_300937
literal: OntoDM_300937

AnnotationAssertion
IRI: OntoDM_300937
literal: GC tree-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a global classifier (GC) approach.

AnnotationAssertion
IRI: OntoDM_300937
literal: GC tree based HC algorithm for single path prediction

AnnotationAssertion
IRI: OntoDM_308048
literal: OntoDM_308048s

AnnotationAssertion
IRI: OntoDM_308048
literal: OntoDM_308048

AnnotationAssertion
IRI: OntoDM_308048
literal: hierarchical classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a labeled tree with boolean edges and discrete nodes datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_308048
literal: tree-based hierarchical classification dataset

AnnotationAssertion
IRI: OntoDM_311225
literal: OntoDM_311225s

AnnotationAssertion
IRI: OntoDM_311225
literal: OntoDM_311225

AnnotationAssertion
IRI: OntoDM_311225
literal: DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model.

AnnotationAssertion
IRI: OntoDM_311225
literal: DAG based HC algorithm

AnnotationAssertion
IRI: OntoDM_316568
literal: OntoDM_316568s

AnnotationAssertion
IRI: OntoDM_316568
literal: OntoDM_316568

AnnotationAssertion
IRI: OntoDM_316568
literal: A binary decision tree is a binary classification model and denotes predictive models that are built on data having a boolean datatype on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and it is expressed in the language of decision trees.

AnnotationAssertion
IRI: OntoDM_316568
literal: binary decision tree

AnnotationAssertion
IRI: OntoDM_318626
literal: OntoDM_318626s

AnnotationAssertion
IRI: OntoDM_318626
literal: OntoDM_318626

AnnotationAssertion
IRI: OntoDM_318626
literal: Pattern set ensemble specification is a ensemble specification and denotes a type of generalization that is produced by a pattern set ensemble algorithm. This algorithm produces a collection of pattern sets at its output.

AnnotationAssertion
IRI: OntoDM_318626
literal: pattern set ensemble specification

AnnotationAssertion
IRI: OntoDM_319168
literal: OntoDM_319168s

AnnotationAssertion
IRI: OntoDM_319168
literal: OntoDM_319168

AnnotationAssertion
IRI: OntoDM_319168
literal: LCL DAG-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_319168
literal: LCL DAG based HC algorithm for multiple path prediction

AnnotationAssertion
IRI: OntoDM_328701
literal: OntoDM_328701s

AnnotationAssertion
IRI: OntoDM_328701
literal: OntoDM_328701

AnnotationAssertion
IRI: OntoDM_328701
literal: LCN DAG-based hierarchical classification algorithm is a data mining algorithm that solves a DAG-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_328701
literal: LCN DAG based HC algorithm

AnnotationAssertion
IRI: OntoDM_330210
literal: OntoDM_330210s

AnnotationAssertion
IRI: OntoDM_330210
literal: OntoDM_330210

AnnotationAssertion
IRI: OntoDM_330210
literal: specification entity

AnnotationAssertion
IRI: OntoDM_341564
literal: OntoDM_341564s

AnnotationAssertion
IRI: OntoDM_341564
literal: OntoDM_341564

AnnotationAssertion
IRI: OntoDM_341564
literal: output tuple of primitives specification is an output structured datatype specification that denotes a tuple of primitives datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_341564
literal: output:tuple of primitives

AnnotationAssertion
IRI: OntoDM_352467
literal: OntoDM_352467s

AnnotationAssertion
IRI: OntoDM_352467
literal: OntoDM_352467

AnnotationAssertion
IRI: OntoDM_352467
literal: output tuple of discrete specification is an output tuple of primitives specification that denotes a tuple of discrete datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_352467
literal: output:tuple of discrete

AnnotationAssertion
IRI: OntoDM_353154
literal: OntoDM_353154s

AnnotationAssertion
IRI: OntoDM_353154
literal: OntoDM_353154

AnnotationAssertion
IRI: OntoDM_353154
literal: LCN tree-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_353154
literal: LCN tree based HC algorithm for single path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_354620
literal: OntoDM_354620s

AnnotationAssertion
IRI: OntoDM_354620
literal: OntoDM_354620

AnnotationAssertion
IRI: OntoDM_354620
literal: Feature-based structured output prediction task is a predicting structured output task where the objective is to predict a structured output (represented with a output structured datatype) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_354620
literal: supervised feature-based structured output prediction task

AnnotationAssertion
IRI: OntoDM_357792
literal: OntoDM_357792s

AnnotationAssertion
IRI: OntoDM_357792
literal: OntoDM_357792

AnnotationAssertion
IRI: OntoDM_357792
literal: Random Subspaces

AnnotationAssertion
IRI: OntoDM_361570
literal: OntoDM_361570s

AnnotationAssertion
IRI: OntoDM_361570
literal: OntoDM_361570

AnnotationAssertion
IRI: OntoDM_361570
literal: data processing algorithm

AnnotationAssertion
IRI: OntoDM_368254
literal: OntoDM_368254s

AnnotationAssertion
IRI: OntoDM_368254
literal: OntoDM_368254

AnnotationAssertion
IRI: OntoDM_368254
literal: Time-series prediction model is afeature-based predictive model for structured output and denotes predictive models that are built on data having a sequence of reals on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_368254
literal: time series prediction model

AnnotationAssertion
IRI: OntoDM_368779
literal: OntoDM_368779s

AnnotationAssertion
IRI: OntoDM_368779
literal: OntoDM_368779

AnnotationAssertion
IRI: OntoDM_368779
literal: Predictive modeling ensemble algorithm is a data mining algorithm that solves a predictive modeling task and as a result produces a ensemble of predictive models.

AnnotationAssertion
IRI: OntoDM_368779
literal: predictive modelling ensemble algorithm

AnnotationAssertion
IRI: OntoDM_369100
literal: OntoDM_369100s

AnnotationAssertion
IRI: OntoDM_369100
literal: OntoDM_369100

AnnotationAssertion
IRI: OntoDM_369100
literal: A multi-class decision tree is a multi-class classification model and denotes predictive models that are built on data having a discrete datatype on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and it is expressed in the language of decision trees.

AnnotationAssertion
IRI: OntoDM_369100
literal: multi-class decision tree

AnnotationAssertion
IRI: OntoDM_372340
literal: OntoDM_372340s

AnnotationAssertion
IRI: OntoDM_372340
literal: OntoDM_372340

AnnotationAssertion
IRI: OntoDM_372340
literal: DAG based hierarchical classification task with multiple label paths is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels.

AnnotationAssertion
IRI: OntoDM_372340
literal: DAG based hierarchical classification task with multiple label paths

AnnotationAssertion
IRI: OntoDM_372936
literal: OntoDM_372936s

AnnotationAssertion
IRI: OntoDM_372936
literal: OntoDM_372936

AnnotationAssertion
IRI: OntoDM_372936
literal: labeled dataset is a dataset specification for datasets that have both a descriptive and output specification of the data examples contained in the dataset.

AnnotationAssertion
IRI: OntoDM_372936
literal: labeled dataset

AnnotationAssertion
IRI: OntoDM_373964
literal: OntoDM_373964s

AnnotationAssertion
IRI: OntoDM_373964
literal: OntoDM_373964

AnnotationAssertion
IRI: OntoDM_373964
literal: Tree based hierarchical classification task with multiple label paths and full depth labeling is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output treewith boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels and all instances have full depth of labeling.

AnnotationAssertion
IRI: OntoDM_373964
literal: tree-based hierarchical classification task with multiple label paths and full depth labeling

AnnotationAssertion
IRI: OntoDM_374511
literal: OntoDM_374511s

AnnotationAssertion
IRI: OntoDM_374511
literal: OntoDM_374511

AnnotationAssertion
IRI: OntoDM_374511
literal: Time-series prediction decision tree is a time-series prediction model and denotes predictive models that are built on data having a sequence of reals on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and is expressed in the language of decision trees.

AnnotationAssertion
IRI: OntoDM_374511
literal: time-series prediction decision tree

AnnotationAssertion
IRI: OntoDM_376810
literal: OntoDM_376810s

AnnotationAssertion
IRI: OntoDM_376810
literal: OntoDM_376810

AnnotationAssertion
IRI: OntoDM_376810
literal: LCN tree-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_376810
literal: LPCN tree based HC algorithm for single path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_379153
literal: OntoDM_379153s

AnnotationAssertion
IRI: OntoDM_379153
literal: OntoDM_379153

AnnotationAssertion
IRI: OntoDM_379153
literal: descriptive tuple of primitives specification is a descriptive datatype specification that denotes a tuple of primitives datatype on the descriptive part of the data.

AnnotationAssertion
IRI: OntoDM_379153
literal: feature-based descriptive data specification

AnnotationAssertion
IRI: OntoDM_382943
literal: OntoDM_382943s

AnnotationAssertion
IRI: OntoDM_382943
literal: OntoDM_382943

AnnotationAssertion
IRI: OntoDM_382943
literal: Predictive modeling algorithm for primitive output is a data mining algorithm that solves a predicting primitive output task and as a result produces a predictive model for primitive output.

AnnotationAssertion
IRI: OntoDM_382943
literal: predictive modeling algorithm for primitive output

AnnotationAssertion
IRI: OntoDM_387728
literal: OntoDM_387728s

AnnotationAssertion
IRI: OntoDM_387728
literal: OntoDM_387728

AnnotationAssertion
IRI: OntoDM_387728
literal: flat classification task is a feature-based primitive output task where the objective is to predict a boolean or discrete output (represented with a boolean or discrete datatype) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_387728
literal: supervised flat classification task

AnnotationAssertion
IRI: OntoDM_388961
literal: OntoDM_388961s

AnnotationAssertion
IRI: OntoDM_388961
literal: OntoDM_388961

AnnotationAssertion
IRI: OntoDM_388961
literal: LCL tree-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_388961
literal: LCL tree based HC algorithm for multiple path prediction

AnnotationAssertion
IRI: OntoDM_397256
literal: OntoDM_397256

AnnotationAssertion
IRI: OntoDM_397256
literal: boolean datatype

AnnotationAssertion
IRI: OntoDM_398436
literal: OntoDM_398436s

AnnotationAssertion
IRI: OntoDM_398436
literal: OntoDM_398436

AnnotationAssertion
IRI: OntoDM_398436
literal: GC tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model using a global classifier (GC) approach.

AnnotationAssertion
IRI: OntoDM_398436
literal: GC tree-based HC algorithm

AnnotationAssertion
IRI: OntoDM_400000
literal: data specification

AnnotationAssertion
IRI: OntoDM_400001
literal: batch data mining task

AnnotationAssertion
IRI: OntoDM_400002
literal: online data mining task

AnnotationAssertion
IRI: OntoDM_400003
literal: online clustering task

AnnotationAssertion
IRI: OntoDM_400004
literal: online pattern discovery task

AnnotationAssertion
IRI: OntoDM_400005
literal: online probability distribution estimation task

AnnotationAssertion
IRI: OntoDM_408772
literal: OntoDM_408772s

AnnotationAssertion
IRI: OntoDM_408772
literal: OntoDM_408772

AnnotationAssertion
IRI: OntoDM_408772
literal: LCN DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_408772
literal: LCN DAG based HC algorithm for multiple path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_419418
literal: OntoDM_419418s

AnnotationAssertion
IRI: OntoDM_419418
literal: OntoDM_419418

AnnotationAssertion
IRI: OntoDM_419418
literal: Tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model.

AnnotationAssertion
IRI: OntoDM_419418
literal: tree-based HC algorithm

AnnotationAssertion
IRI: OntoDM_419460
literal: OntoDM_419460s

AnnotationAssertion
IRI: OntoDM_419460
literal: OntoDM_419460

AnnotationAssertion
IRI: OntoDM_419460
literal: A single generalization specification is a generalization specification and denotes a type of generalization that is produced by a single generalization data mining algorithm. This algorithm produces only one generalization at its output.

AnnotationAssertion
IRI: OntoDM_419460
literal: single generalization specification

AnnotationAssertion
IRI: OntoDM_419544
literal: OntoDM_419544s

AnnotationAssertion
IRI: OntoDM_419544
literal: OntoDM_419544

AnnotationAssertion
IRI: OntoDM_419544
literal: information content entity quality

AnnotationAssertion
IRI: OntoDM_429658
literal: OntoDM_429658s

AnnotationAssertion
IRI: OntoDM_429658
literal: OntoDM_429658

AnnotationAssertion
IRI: OntoDM_429658
literal: Clustering ensemble algorithm is a data mining algorithm that solves a clustering task and as a result produces an ensemble of clusterings.

AnnotationAssertion
IRI: OntoDM_429658
literal: clustering ensemble algorithm

AnnotationAssertion
IRI: OntoDM_433774
literal: OntoDM_433774s

AnnotationAssertion
IRI: OntoDM_433774
literal: OntoDM_433774

AnnotationAssertion
IRI: OntoDM_433774
literal: language of markov chains

AnnotationAssertion
IRI: OntoDM_452759
literal: OntoDM_452759s

AnnotationAssertion
IRI: OntoDM_452759
literal: OntoDM_452759

AnnotationAssertion
IRI: OntoDM_452759
literal: Bagging

AnnotationAssertion
IRI: OntoDM_458780
literal: OntoDM_458780

AnnotationAssertion
IRI: OntoDM_458780
literal: C++

AnnotationAssertion
IRI: OntoDM_458784
literal: OntoDM_458784s

AnnotationAssertion
IRI: OntoDM_458784
literal: OntoDM_458784

AnnotationAssertion
IRI: OntoDM_458784
literal: output tree specification is an output labeled graph specification that denotes a tree datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_458784
literal: output:tree

AnnotationAssertion
IRI: OntoDM_462575
literal: OntoDM_462575s

AnnotationAssertion
IRI: OntoDM_462575
literal: OntoDM_462575

AnnotationAssertion
IRI: OntoDM_462575
literal: Time-series predictive modeling algorithm is a data mining algorithm that solves a time-series classification task and as a result produces a time series predictive model.

AnnotationAssertion
IRI: OntoDM_462575
literal: time-series predictive modelling algorithm

AnnotationAssertion
IRI: OntoDM_464319
literal: OntoDM_464319s

AnnotationAssertion
IRI: OntoDM_464319
literal: OntoDM_464319

AnnotationAssertion
IRI: OntoDM_464319
literal: LCN tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_464319
literal: LCN tree based HC algorithm

AnnotationAssertion
IRI: OntoDM_464484
literal: OntoDM_464484s

AnnotationAssertion
IRI: OntoDM_464484
literal: OntoDM_464484

AnnotationAssertion
IRI: OntoDM_464484
literal: Probability distribution estimation ensemble algorithm is a data mining algorithm that solves a probablility distribution estimation task and as a result produces a ensemble of probability distributions.

AnnotationAssertion
IRI: OntoDM_464484
literal: probability distribution estimation ensemble algorithm

AnnotationAssertion
IRI: OntoDM_467074
literal: OntoDM_467074s

AnnotationAssertion
IRI: OntoDM_467074
literal: OntoDM_467074

AnnotationAssertion
IRI: OntoDM_467074
literal: LCPN DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_467074
literal: LCPN DAG based HC algorithm for multiple path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_470460
literal: OntoDM_470460s

AnnotationAssertion
IRI: OntoDM_470460
literal: OntoDM_470460

AnnotationAssertion
IRI: OntoDM_470460
literal: LCPN tree-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_470460
literal: LPCN tree based HC algorithm for single path prediction

AnnotationAssertion
IRI: OntoDM_474912
literal: OntoDM_474912s

AnnotationAssertion
IRI: OntoDM_474912
literal: OntoDM_474912

AnnotationAssertion
IRI: OntoDM_474912
literal: LCL tree-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_474912
literal: LCL tree based HC algorithm for single path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_478450
literal: OntoDM_478450s

AnnotationAssertion
IRI: OntoDM_478450
literal: OntoDM_478450

AnnotationAssertion
IRI: OntoDM_478450
literal: workflow

AnnotationAssertion
IRI: OntoDM_481568
literal: OntoDM_481568s

AnnotationAssertion
IRI: OntoDM_481568
literal: OntoDM_481568

AnnotationAssertion
IRI: OntoDM_481568
literal: Multi-target multi-class classification model is a multi-target classification model and denotes predictive models that are built on data having a tuple of discrete (set of output features) on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_481568
literal: multi-target multi-class classification model

AnnotationAssertion
IRI: OntoDM_485256
literal: OntoDM_485256s

AnnotationAssertion
IRI: OntoDM_485256
literal: OntoDM_485256

AnnotationAssertion
IRI: OntoDM_485256
literal: GC DAG-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a global classifier (GC) approach.

AnnotationAssertion
IRI: OntoDM_485256
literal: GC DAG based HC algorithm for multiple path prediction

AnnotationAssertion
IRI: OntoDM_490530
literal: OntoDM_490530s

AnnotationAssertion
IRI: OntoDM_490530
literal: OntoDM_490530

AnnotationAssertion
IRI: OntoDM_490530
literal: clusterings ensemble specification is a ensemble specification and denotes a type of generalization that is produced by a clustering ensemble algorithm. This algorithm produces a set of clusterings at its output.

AnnotationAssertion
IRI: OntoDM_490530
literal: clusterings ensemble specification

AnnotationAssertion
IRI: OntoDM_491182
literal: OntoDM_491182s

AnnotationAssertion
IRI: OntoDM_491182
literal: OntoDM_491182

AnnotationAssertion
IRI: OntoDM_491182
literal: DAG based hierarchical classification task is a hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_491182
literal: supervised DAG based hierarchical classification task

AnnotationAssertion
IRI: OntoDM_495960
literal: OntoDM_495960s

AnnotationAssertion
IRI: OntoDM_495960
literal: OntoDM_495960

AnnotationAssertion
IRI: OntoDM_495960
literal: Multi-target classification model is a multi-target prediction model and denotes predictive models that are built on data having a tuple of boolean or discrete (set of output features) on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_495960
literal: multi-target classification model

AnnotationAssertion
IRI: OntoDM_506961
literal: OntoDM_506961s

AnnotationAssertion
IRI: OntoDM_506961
literal: OntoDM_506961

AnnotationAssertion
IRI: OntoDM_506961
literal: Multi-target regression task is a multi-target prediction task where the objective is to predict a set of real features (represented with a output tuple of reals) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_506961
literal: supervised multi-target regression task

AnnotationAssertion
IRI: OntoDM_507935
literal: OntoDM_507935s

AnnotationAssertion
IRI: OntoDM_507935
literal: OntoDM_507935

AnnotationAssertion
IRI: OntoDM_507935
literal: Hierarchical classification algorithm is a data mining algorithm that solves a hierarchical classification task and as a result produces a hierarchical classification model.

AnnotationAssertion
IRI: OntoDM_507935
literal: hierarchical classification algorithm

AnnotationAssertion
IRI: OntoDM_510504
literal: OntoDM_510504s

AnnotationAssertion
IRI: OntoDM_510504
literal: OntoDM_510504

AnnotationAssertion
IRI: OntoDM_510504
literal: Multi-label classification task is a feature-based structured output prediction task where the objective is to predict a set of discrete output labels (represented with a output set of discrete datatype) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_510504
literal: supervised multi-label classification task

AnnotationAssertion
IRI: OntoDM_510989
literal: OntoDM_510989s

AnnotationAssertion
IRI: OntoDM_510989
literal: OntoDM_510989

AnnotationAssertion
IRI: OntoDM_510989
literal: a parameter setting is a data item that is a quality specification of some algorithm parameter and denotes its configuration when an algorithm implementation is used as a data mining operator in the process of algorithm execution.

AnnotationAssertion
IRI: OntoDM_510989
literal: parameter setting

AnnotationAssertion
IRI: OntoDM_512674
literal: OntoDM_512674s

AnnotationAssertion
IRI: OntoDM_512674
literal: OntoDM_512674

AnnotationAssertion
IRI: OntoDM_512674
literal: LCN tree-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_512674
literal: LCN tree based HC algorithm for single path prediction

AnnotationAssertion
IRI: OntoDM_516954
literal: OntoDM_516954s

AnnotationAssertion
IRI: OntoDM_516954
literal: OntoDM_516954

AnnotationAssertion
IRI: OntoDM_516954
literal: output sequence specification is an output structured datatype specification that denotes a sequence datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_516954
literal: output:sequence

AnnotationAssertion
IRI: OntoDM_519526
literal: OntoDM_519526s

AnnotationAssertion
IRI: OntoDM_519526
literal: OntoDM_519526

AnnotationAssertion
IRI: OntoDM_519526
literal: GC tree based HC algorithm for single path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_528443
literal: OntoDM_528443s

AnnotationAssertion
IRI: OntoDM_528443
literal: OntoDM_528443

AnnotationAssertion
IRI: OntoDM_528443
literal: LCL tree-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_528443
literal: LCL tree based HC algorithm for multiple path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_530819
literal: OntoDM_530819s

AnnotationAssertion
IRI: OntoDM_530819
literal: OntoDM_530819

AnnotationAssertion
IRI: OntoDM_530819
literal: A ensemble algorithm is a data mining algorithm that solves a data mining task and as a result produces an ensemble of generalizations.

AnnotationAssertion
IRI: OntoDM_530819
literal: ensemble algorithm

AnnotationAssertion
IRI: OntoDM_532190
literal: OntoDM_532190s

AnnotationAssertion
IRI: OntoDM_532190
literal: OntoDM_532190

AnnotationAssertion
IRI: OntoDM_532190
literal: language of neural nets

AnnotationAssertion
IRI: OntoDM_535716
literal: OntoDM_535716s

AnnotationAssertion
IRI: OntoDM_535716
literal: OntoDM_535716

AnnotationAssertion
IRI: OntoDM_535716
literal: Probability distributions specification is a ensemble specification and denotes a type of generalization that is produced by a probability distribution estimation ensemble algorithm. This algorithm produces a set of probability distributions at its output.

AnnotationAssertion
IRI: OntoDM_535716
literal: probability distributions ensemble specification

AnnotationAssertion
IRI: OntoDM_537602
literal: OntoDM_537602s

AnnotationAssertion
IRI: OntoDM_537602
literal: OntoDM_537602

AnnotationAssertion
IRI: OntoDM_537602
literal: labeled feature-based dataset with primitive output is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a primitive datatype on the output part of the data (specified with the output feature specification).

AnnotationAssertion
IRI: OntoDM_537602
literal: feature-based completely labeled dataset with primitive output

AnnotationAssertion
IRI: OntoDM_540049
literal: OntoDM_540049s

AnnotationAssertion
IRI: OntoDM_540049
literal: OntoDM_540049

AnnotationAssertion
IRI: OntoDM_540049
literal: LC tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier (LC) approach.

AnnotationAssertion
IRI: OntoDM_540049
literal: LC tree based HC algorithm

AnnotationAssertion
IRI: OntoDM_541005
literal: OntoDM_541005s

AnnotationAssertion
IRI: OntoDM_541005
literal: OntoDM_541005

AnnotationAssertion
IRI: OntoDM_541005
literal: multi-label classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a set of discrete datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_541005
literal: multi-label classification dataset

AnnotationAssertion
IRI: OntoDM_542264
literal: OntoDM_542264s

AnnotationAssertion
IRI: OntoDM_542264
literal: OntoDM_542264

AnnotationAssertion
IRI: OntoDM_542264
literal: Tree based hierarchical classification task is a hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_542264
literal: supervised tree-based hierarchical classification task

AnnotationAssertion
IRI: OntoDM_544234
literal: OntoDM_544234s

AnnotationAssertion
IRI: OntoDM_544234
literal: OntoDM_544234

AnnotationAssertion
IRI: OntoDM_544234
literal: data mining software toolkit

AnnotationAssertion
IRI: OntoDM_548678
literal: OntoDM_548678s

AnnotationAssertion
IRI: OntoDM_548678
literal: OntoDM_548678

AnnotationAssertion
IRI: OntoDM_548678
literal: A data mining task description is-a specifcation entity that denotes a specific data mining task applied on a specific dataset, having in mind the specific type of generalization produced by the task (generalization specifcation)

AnnotationAssertion
IRI: OntoDM_548678
literal: data mining task description

AnnotationAssertion
IRI: OntoDM_548720
literal: OntoDM_548720s

AnnotationAssertion
IRI: OntoDM_548720
literal: OntoDM_548720

AnnotationAssertion
IRI: OntoDM_548720
literal: data mining source code module

AnnotationAssertion
IRI: OntoDM_549140
literal: OntoDM_549140s

AnnotationAssertion
IRI: OntoDM_549140
literal: OntoDM_549140

AnnotationAssertion
IRI: OntoDM_549140
literal: LCPN tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_549140
literal: LCPN tree based HC algorithm

AnnotationAssertion
IRI: OntoDM_555011
literal: OntoDM_555011s

AnnotationAssertion
IRI: OntoDM_555011
literal: OntoDM_555011

AnnotationAssertion
IRI: OntoDM_555011
literal: LCN DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_555011
literal: LCN DAG based HC algorithm for single path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_559103
literal: OntoDM_559103s

AnnotationAssertion
IRI: OntoDM_559103
literal: OntoDM_559103

AnnotationAssertion
IRI: OntoDM_559103
literal: Tree based hierarchical classification task with single label paths is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have a single paths of labels.

AnnotationAssertion
IRI: OntoDM_559103
literal: tree-based hierarchical classification task with single label paths

AnnotationAssertion
IRI: OntoDM_565974
literal: OntoDM_565974s

AnnotationAssertion
IRI: OntoDM_565974
literal: OntoDM_565974

AnnotationAssertion
IRI: OntoDM_565974
literal: multi-target regression dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a tuple of reals (specified with the feature set specification) on the output part of the data.

AnnotationAssertion
IRI: OntoDM_565974
literal: multi-target regression dataset

AnnotationAssertion
IRI: OntoDM_570767
literal: OntoDM_570767s

AnnotationAssertion
IRI: OntoDM_570767
literal: OntoDM_570767

AnnotationAssertion
IRI: OntoDM_570767
literal: Multi-label classification algorithm is a data mining algorithm that solves a multi-label classification task and as a result produces a multi-label classification model.

AnnotationAssertion
IRI: OntoDM_570767
literal: multi-label classification algorithm

AnnotationAssertion
IRI: OntoDM_572839
literal: OntoDM_572839s

AnnotationAssertion
IRI: OntoDM_572839
literal: OntoDM_572839

AnnotationAssertion
IRI: OntoDM_572839
literal: LCL tree-based hierarchical classification algorithm for single path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_572839
literal: LCL tree based HC algorithm for single path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_577929
literal: OntoDM_577929s

AnnotationAssertion
IRI: OntoDM_577929
literal: OntoDM_577929

AnnotationAssertion
IRI: OntoDM_577929
literal: A predictive model for structured output is a predictive model specification and denotes predictive models that are built on data having a structured datatype on the output part.

AnnotationAssertion
IRI: OntoDM_577929
literal: predictive model for structured output

AnnotationAssertion
IRI: OntoDM_583334
literal: OntoDM_583334s

AnnotationAssertion
IRI: OntoDM_583334
literal: OntoDM_583334

AnnotationAssertion
IRI: OntoDM_583334
literal: Multi-target regression model is a multi-target prediction model and denotes predictive models that are built on data having a tuple of reals (set of output features) on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_583334
literal: multi-target regression model

AnnotationAssertion
IRI: OntoDM_585235
literal: OntoDM_585235s

AnnotationAssertion
IRI: OntoDM_585235
literal: OntoDM_585235

AnnotationAssertion
IRI: OntoDM_585235
literal: LCL DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_585235
literal: LCL DAG based HC algorithm for multiple path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_592921
literal: OntoDM_592921

AnnotationAssertion
IRI: OntoDM_592921
literal: HMC-ENS-Bagging algorithm

AnnotationAssertion
IRI: OntoDM_600958
literal: OntoDM_600958s

AnnotationAssertion
IRI: OntoDM_600958
literal: OntoDM_600958

AnnotationAssertion
IRI: OntoDM_600958
literal: predicting structured output task is a predictive modeling task where the objective is to predict a structured output (represented with a structured datatype).

AnnotationAssertion
IRI: OntoDM_600958
literal: supervised structured output prediction task

AnnotationAssertion
IRI: OntoDM_603253
literal: OntoDM_603253s

AnnotationAssertion
IRI: OntoDM_603253
literal: OntoDM_603253

AnnotationAssertion
IRI: OntoDM_603253
literal: scenario

AnnotationAssertion
IRI: OntoDM_608558
literal: OntoDM_608558s

AnnotationAssertion
IRI: OntoDM_608558
literal: OntoDM_608558

AnnotationAssertion
IRI: OntoDM_608558
literal: unlabeled feature-based dataset is a dataset specification of unlabeled dataset with tuple of primitives as a descriptive datatype specification and descriptive feature set specification.

AnnotationAssertion
IRI: OntoDM_608558
literal: feature-based unlabeled dataset

AnnotationAssertion
IRI: OntoDM_609184
literal: OntoDM_609184s

AnnotationAssertion
IRI: OntoDM_609184
literal: OntoDM_609184

AnnotationAssertion
IRI: OntoDM_609184
literal: binary classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a boolean datatype on the output part of the data (specified with the feature specification).

AnnotationAssertion
IRI: OntoDM_609184
literal: binary classification dataset

AnnotationAssertion
IRI: OntoDM_611937
literal: OntoDM_611937s

AnnotationAssertion
IRI: OntoDM_611937
literal: OntoDM_611937

AnnotationAssertion
IRI: OntoDM_611937
literal: GC tree based HC algorithm for single path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_613600
literal: OntoDM_613600s

AnnotationAssertion
IRI: OntoDM_613600
literal: OntoDM_613600

AnnotationAssertion
IRI: OntoDM_613600
literal: clustering evaluation

AnnotationAssertion
IRI: OntoDM_613675
literal: OntoDM_613675s

AnnotationAssertion
IRI: OntoDM_613675
literal: OntoDM_613675

AnnotationAssertion
IRI: OntoDM_613675
literal: LCPN DAG-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_613675
literal: LCPN DAG based HC algorithm for single path prediction

AnnotationAssertion
IRI: OntoDM_613950
literal: OntoDM_613950s

AnnotationAssertion
IRI: OntoDM_613950
literal: OntoDM_613950

AnnotationAssertion
IRI: OntoDM_613950
literal: language of graphical models

AnnotationAssertion
IRI: OntoDM_616457
literal: OntoDM_616457s

AnnotationAssertion
IRI: OntoDM_616457
literal: OntoDM_616457

AnnotationAssertion
IRI: OntoDM_616457
literal: language of decision rules

AnnotationAssertion
IRI: OntoDM_616614
literal: OntoDM_616614s

AnnotationAssertion
IRI: OntoDM_616614
literal: OntoDM_616614

AnnotationAssertion
IRI: OntoDM_616614
literal: software toolkit

AnnotationAssertion
IRI: OntoDM_620805
literal: OntoDM_620805s

AnnotationAssertion
IRI: OntoDM_620805
literal: OntoDM_620805

AnnotationAssertion
IRI: OntoDM_620805
literal: DAG based hierarchical classification task with single label paths and full depth labeling is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have single paths of labels and all instances have full depth of labeling.

AnnotationAssertion
IRI: OntoDM_620805
literal: DAG based hierarchical classification task with single label paths and full depth labeling

AnnotationAssertion
IRI: OntoDM_622093
literal: OntoDM_622093s

AnnotationAssertion
IRI: OntoDM_622093
literal: OntoDM_622093

AnnotationAssertion
IRI: OntoDM_622093
literal: LCL tree-based hierarchical classification algorithm for multiple path and mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_622093
literal: LCL tree based HC algorithm for multiple path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_622428
literal: OntoDM_622428s

AnnotationAssertion
IRI: OntoDM_622428
literal: OntoDM_622428

AnnotationAssertion
IRI: OntoDM_622428
literal: Tree based hierarchical classification task with single label paths and full depth labeling is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have single paths of labels and all instances have full depth of labeling.

AnnotationAssertion
IRI: OntoDM_622428
literal: tree-based hierarchical classification task with single label paths and full depth labeling

AnnotationAssertion
IRI: OntoDM_625176
literal: OntoDM_625176s

AnnotationAssertion
IRI: OntoDM_625176
literal: OntoDM_625176

AnnotationAssertion
IRI: OntoDM_625176
literal: evaluation algorithm implementation

AnnotationAssertion
IRI: OntoDM_627978
literal: OntoDM_627978s

AnnotationAssertion
IRI: OntoDM_627978
literal: OntoDM_627978

AnnotationAssertion
IRI: OntoDM_627978
literal: Multi-target binary decision tree is a multi-target binary classification model and denotes predictive models that are built on data having a tuple of boolean (set of output features) on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and is expressed in the language of decision trees.

AnnotationAssertion
IRI: OntoDM_627978
literal: multi-target binary decision tree

AnnotationAssertion
IRI: OntoDM_629944
literal: OntoDM_629944s

AnnotationAssertion
IRI: OntoDM_629944
literal: OntoDM_629944

AnnotationAssertion
IRI: OntoDM_629944
literal: labeled dataset with structured output is a dataset specification of a labeled dataset with structured output datatype specification.

AnnotationAssertion
IRI: OntoDM_629944
literal: completely labeled dataset with structured output

AnnotationAssertion
IRI: OntoDM_636213
literal: OntoDM_636213s

AnnotationAssertion
IRI: OntoDM_636213
literal: OntoDM_636213

AnnotationAssertion
IRI: OntoDM_636213
literal: multi-target multi-class classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a tuple of discrete (specified with the feature set specification) on the output part of the data.

AnnotationAssertion
IRI: OntoDM_636213
literal: multi-target multi-class classification dataset

AnnotationAssertion
IRI: OntoDM_636747
literal: OntoDM_636747s

AnnotationAssertion
IRI: OntoDM_636747
literal: OntoDM_636747

AnnotationAssertion
IRI: OntoDM_636747
literal: multi-class classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part, and a discrete datatype on the output part of the data (specified with the feature specification).

AnnotationAssertion
IRI: OntoDM_636747
literal: multi-class classification dataset

AnnotationAssertion
IRI: OntoDM_640624
literal: OntoDM_640624s

AnnotationAssertion
IRI: OntoDM_640624
literal: OntoDM_640624

AnnotationAssertion
IRI: OntoDM_640624
literal: Hierarchical classification decision tree is a hierarchical classification model and denotes predictive models that are built on data having a labeled graph datatype on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and it is expressed in the language of decision trees.

AnnotationAssertion
IRI: OntoDM_640624
literal: hierarchical classification decision tree

AnnotationAssertion
IRI: OntoDM_642324
literal: OntoDM_642324s

AnnotationAssertion
IRI: OntoDM_642324
literal: OntoDM_642324

AnnotationAssertion
IRI: OntoDM_642324
literal: language of decision trees

AnnotationAssertion
IRI: OntoDM_651414
literal: OntoDM_651414s

AnnotationAssertion
IRI: OntoDM_651414
literal: OntoDM_651414

AnnotationAssertion
IRI: OntoDM_651414
literal: a predicted dataset is a dataset that is an output of a predictive model execution process.

AnnotationAssertion
IRI: OntoDM_651414
literal: predicted dataset

AnnotationAssertion
IRI: OntoDM_661861
literal: OntoDM_661861s

AnnotationAssertion
IRI: OntoDM_661861
literal: OntoDM_661861

AnnotationAssertion
IRI: OntoDM_661861
literal: structure based structured output prediction task is a predicting structured output task where the objective is to predict a structured output (represented with a outputstructured datatype) from a structure (represented by a descriptive structured datatype).

AnnotationAssertion
IRI: OntoDM_661861
literal: supervised structure based structured output prediction task

AnnotationAssertion
IRI: OntoDM_666475
literal: OntoDM_666475s

AnnotationAssertion
IRI: OntoDM_666475
literal: OntoDM_666475

AnnotationAssertion
IRI: OntoDM_666475
literal: labeled feature-based dataset with structured output is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a structured datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_666475
literal: feature-based completely labeled dataset with structured output

AnnotationAssertion
IRI: OntoDM_669483
literal: OntoDM_669483s

AnnotationAssertion
IRI: OntoDM_669483
literal: OntoDM_669483

AnnotationAssertion
IRI: OntoDM_669483
literal: LCL tree-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_669483
literal: LCL tree based HC algorithm for single path prediction

AnnotationAssertion
IRI: OntoDM_674255
literal: OntoDM_674255s

AnnotationAssertion
IRI: OntoDM_674255
literal: OntoDM_674255

AnnotationAssertion
IRI: OntoDM_674255
literal: Multi-target regression algorithm is a data mining algorithm that solves a multi-target regression task and as a result produces a multi-target regression model.

AnnotationAssertion
IRI: OntoDM_674255
literal: multi-target regression algorithm

AnnotationAssertion
IRI: OntoDM_675141
literal: OntoDM_675141s

AnnotationAssertion
IRI: OntoDM_675141
literal: OntoDM_675141

AnnotationAssertion
IRI: OntoDM_675141
literal: descriptive set of discrete is a descriptive structured datatype specification that denotes a set of discrete datatype on the descriptive part of the data.

AnnotationAssertion
IRI: OntoDM_675141
literal: descriptive:set of discrete

AnnotationAssertion
IRI: OntoDM_686944
literal: OntoDM_686944s

AnnotationAssertion
IRI: OntoDM_686944
literal: OntoDM_686944

AnnotationAssertion
IRI: OntoDM_686944
literal: regression dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a real datatype on the output part of the data (specified with the output specification).

AnnotationAssertion
IRI: OntoDM_686944
literal: regression dataset

AnnotationAssertion
IRI: OntoDM_688833
literal: OntoDM_688833s

AnnotationAssertion
IRI: OntoDM_688833
literal: OntoDM_688833

AnnotationAssertion
IRI: OntoDM_688833
literal: A regression model is a feature-based predictive model for primitive output and denotes predictive models that are built on data having a real datatype on the output part, and set of features (represented by a descriptive tuple of primitives) on the descriptive part.

AnnotationAssertion
IRI: OntoDM_688833
literal: regression model

AnnotationAssertion
IRI: OntoDM_689658
literal: OntoDM_689658s

AnnotationAssertion
IRI: OntoDM_689658
literal: OntoDM_689658

AnnotationAssertion
IRI: OntoDM_689658
literal: Multi-target multi-class decision tree is a multi-target multi-class classification model and denotes predictive models that are built on data having a tuple of discrete (set of output features) on the output part, set of features (represented by a descriptive tuple of primitives) on the descriptive part, and are expressed in the language of decision trees.

AnnotationAssertion
IRI: OntoDM_689658
literal: multi-target multi-class decison tree

AnnotationAssertion
IRI: OntoDM_692551
literal: OntoDM_692551s

AnnotationAssertion
IRI: OntoDM_692551
literal: OntoDM_692551

AnnotationAssertion
IRI: OntoDM_692551
literal: LCL DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_692551
literal: LCL DAG based HC algorithm for single path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_692808
literal: OntoDM_692808s

AnnotationAssertion
IRI: OntoDM_692808
literal: OntoDM_692808

AnnotationAssertion
IRI: OntoDM_692808
literal: labeled dataset with structured output is a dataset specification of a labeled dataset with structured datatype on the descriptive part and a structured datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_692808
literal: structure-based completely labeled dataset with structured output

AnnotationAssertion
IRI: OntoDM_693227
literal: OntoDM_693227s

AnnotationAssertion
IRI: OntoDM_693227
literal: OntoDM_693227

AnnotationAssertion
IRI: OntoDM_693227
literal: description

AnnotationAssertion
IRI: OntoDM_694510
literal: OntoDM_694510s

AnnotationAssertion
IRI: OntoDM_694510
literal: OntoDM_694510

AnnotationAssertion
IRI: OntoDM_694510
literal: GC tree-based hierarchical classification algorithm for multiple path prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and as a result produces a hierarchical classification model using a global classifier (GC) approach.

AnnotationAssertion
IRI: OntoDM_694510
literal: GC tree-based HC algorithm for multiple path prediction

AnnotationAssertion
IRI: OntoDM_708865
literal: OntoDM_708865s

AnnotationAssertion
IRI: OntoDM_708865
literal: OntoDM_708865

AnnotationAssertion
IRI: OntoDM_708865
literal: representational medium specification

AnnotationAssertion
IRI: OntoDM_716122
literal: OntoDM_716122s

AnnotationAssertion
IRI: OntoDM_716122
literal: OntoDM_716122

AnnotationAssertion
IRI: OntoDM_716122
literal: output boolean specification is an output primitive datatype specification that denotes a boolean datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_716122
literal: output:boolean

AnnotationAssertion
IRI: OntoDM_718392
literal: OntoDM_718392s

AnnotationAssertion
IRI: OntoDM_718392
literal: OntoDM_718392

AnnotationAssertion
IRI: OntoDM_718392
literal: DAG based hierarchical classification task with multiple label paths and partial depth labeling is a DAG based hierarchical classification task where the objective is to predict a set of labels organized in a DAG (represented with a output DAG with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels and at least one instance has a partial depth of labeling.

AnnotationAssertion
IRI: OntoDM_718392
literal: DAG based hierarchical classification task with multiple label paths and partial depth labeling

AnnotationAssertion
IRI: OntoDM_718446
literal: OntoDM_718446s

AnnotationAssertion
IRI: OntoDM_718446
literal: OntoDM_718446

AnnotationAssertion
IRI: OntoDM_718446
literal: Structure-based predictive modeling algorithm for primitive output is a data mining algorithm that solves a structure-based primitive output prediction task and as a result produces a structure-based predictive model for primitive output.

AnnotationAssertion
IRI: OntoDM_718446
literal: structure-based prectictive modeling algorithm for primitive output

AnnotationAssertion
IRI: OntoDM_721008
literal: OntoDM_721008s

AnnotationAssertion
IRI: OntoDM_721008
literal: OntoDM_721008

AnnotationAssertion
IRI: OntoDM_721008
literal: Random Forest

AnnotationAssertion
IRI: OntoDM_722523
literal: OntoDM_722523s

AnnotationAssertion
IRI: OntoDM_722523
literal: OntoDM_722523

AnnotationAssertion
IRI: OntoDM_722523
literal: Multi-target classification algorithm is a data mining algorithm that solves a multi-target classification task and as a result produces a multi-target classification model.

AnnotationAssertion
IRI: OntoDM_722523
literal: multi-target classification algorithm

AnnotationAssertion
IRI: OntoDM_726490
literal: OntoDM_726490s

AnnotationAssertion
IRI: OntoDM_726490
literal: OntoDM_726490

AnnotationAssertion
IRI: OntoDM_726490
literal: Mapping specification is a information content entity that denotes a part of a data example on which a concrete datatype applies to.

AnnotationAssertion
IRI: OntoDM_726490
literal: mapping specification

AnnotationAssertion
IRI: OntoDM_727807
literal: OntoDM_727807s

AnnotationAssertion
IRI: OntoDM_727807
literal: OntoDM_727807

AnnotationAssertion
IRI: OntoDM_727807
literal: multi-target binary classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a tuple of booleans (specified with the feature set specification) on the output part of the data.

AnnotationAssertion
IRI: OntoDM_727807
literal: multi-target binary classification dataset

AnnotationAssertion
IRI: OntoDM_728533
literal: OntoDM_728533s

AnnotationAssertion
IRI: OntoDM_728533
literal: OntoDM_728533

AnnotationAssertion
IRI: OntoDM_728533
literal: Multi-target binary classification algorithm is a data mining algorithm that solves a multi-target binary classification task and as a result produces a multi-target binarty classification model.

AnnotationAssertion
IRI: OntoDM_728533
literal: multi-target binary classification algorithm

AnnotationAssertion
IRI: OntoDM_730046
literal: OntoDM_730046s

AnnotationAssertion
IRI: OntoDM_730046
literal: OntoDM_730046

AnnotationAssertion
IRI: OntoDM_730046
literal: LCN tree-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a tree-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_730046
literal: LCN tree based HC algorithm for multiple path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_730934
literal: OntoDM_730934s

AnnotationAssertion
IRI: OntoDM_730934
literal: OntoDM_730934

AnnotationAssertion
IRI: OntoDM_730934
literal: evaluation algorithm

AnnotationAssertion
IRI: OntoDM_731692
literal: OntoDM_731692s

AnnotationAssertion
IRI: OntoDM_731692
literal: OntoDM_731692

AnnotationAssertion
IRI: OntoDM_731692
literal: A single generalization algorithm is a data mining algorithm that solves a data mining task and as a result produces a single generalization.

AnnotationAssertion
IRI: OntoDM_731692
literal: single generalization algorithm

AnnotationAssertion
IRI: OntoDM_733255
literal: OntoDM_733255s

AnnotationAssertion
IRI: OntoDM_733255
literal: OntoDM_733255

AnnotationAssertion
IRI: OntoDM_733255
literal: Multi-target classification task is a multi-target prediction task where the objective is to predict a set of binary/discrete features (represented with a output tuple of booleans or discrete) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_733255
literal: supervised multi-target classification task

AnnotationAssertion
IRI: OntoDM_741560
literal: OntoDM_741560

AnnotationAssertion
IRI: OntoDM_741560
literal: OntoDM_741560s

AnnotationAssertion
IRI: OntoDM_741560
literal: OntoDM_741560ed

AnnotationAssertion
IRI: OntoDM_741560
literal: is_representation_of

AnnotationAssertion
IRI: OntoDM_741571
literal: OntoDM_741571s

AnnotationAssertion
IRI: OntoDM_741571
literal: OntoDM_741571

AnnotationAssertion
IRI: OntoDM_741571
literal: output primitive is an output datatype specification that denotes a primitive datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_741571
literal: primitive-based output data specification

AnnotationAssertion
IRI: OntoDM_746568
literal: OntoDM_746568s

AnnotationAssertion
IRI: OntoDM_746568
literal: OntoDM_746568

AnnotationAssertion
IRI: OntoDM_746568
literal: multi-target prediction dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a tuple of primitives (specified with the feature set specification) on the output part of the data.

AnnotationAssertion
IRI: OntoDM_746568
literal: multi-target prediction dataset

AnnotationAssertion
IRI: OntoDM_748173
literal: OntoDM_748173s

AnnotationAssertion
IRI: OntoDM_748173
literal: OntoDM_748173

AnnotationAssertion
IRI: OntoDM_748173
literal: operator

AnnotationAssertion
IRI: OntoDM_754841
literal: OntoDM_754841s

AnnotationAssertion
IRI: OntoDM_754841
literal: OntoDM_754841

AnnotationAssertion
IRI: OntoDM_754841
literal: GC tree based HC algorithm for multiple path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_757135
literal: OntoDM_757135s

AnnotationAssertion
IRI: OntoDM_757135
literal: OntoDM_757135

AnnotationAssertion
IRI: OntoDM_757135
literal: information processing objective is objective specification of the end point that an information processing process needs to achieve at its end.

AnnotationAssertion
IRI: OntoDM_757135
literal: information processing objective

AnnotationAssertion
IRI: OntoDM_757640
literal: OntoDM_757640s

AnnotationAssertion
IRI: OntoDM_757640
literal: OntoDM_757640

AnnotationAssertion
IRI: OntoDM_757640
literal: Multi-target multi-class classification algorithm is a data mining algorithm that solves a multi-target multi-class classification task and as a result produces a multi-target multi-class classification model.

AnnotationAssertion
IRI: OntoDM_757640
literal: multi-target multi-class classification algorithm

AnnotationAssertion
IRI: OntoDM_771971
literal: OntoDM_771971s

AnnotationAssertion
IRI: OntoDM_771971
literal: OntoDM_771971

AnnotationAssertion
IRI: OntoDM_771971
literal: GC tree based HC algorithm for multiple path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_776589
literal: OntoDM_776589s

AnnotationAssertion
IRI: OntoDM_776589
literal: OntoDM_776589

AnnotationAssertion
IRI: OntoDM_776589
literal: OntoDM_776589

AnnotationAssertion
IRI: OntoDM_776589
literal: pattern discovery evaluation

AnnotationAssertion
IRI: OntoDM_782155
literal: OntoDM_782155s

AnnotationAssertion
IRI: OntoDM_782155
literal: OntoDM_782155

AnnotationAssertion
IRI: OntoDM_782155
literal: LCN DAG-based hierarchical classification algorithm for multiple path and mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and full depth labeling and as a result produces a hierarchical classification model using a local classifier per node (LCN) approach.

AnnotationAssertion
IRI: OntoDM_782155
literal: LCN DAG based HC algorithm for multiple path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_783954
literal: OntoDM_783954s

AnnotationAssertion
IRI: OntoDM_783954
literal: OntoDM_783954

AnnotationAssertion
IRI: OntoDM_783954
literal: multi-target classification dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a tuple of booleans or discrete (specified with the feature set specification) on the output part of the data.

AnnotationAssertion
IRI: OntoDM_783954
literal: multi-target classification dataset

AnnotationAssertion
IRI: OntoDM_785336
literal: OntoDM_785336s

AnnotationAssertion
IRI: OntoDM_785336
literal: OntoDM_785336

AnnotationAssertion
IRI: OntoDM_785336
literal: Structure-based primitive output prediction task is a predicting primitive output task where the objective is to predict a primitive output (represented with a primitive datatype) from a structure (represented by a descriptive structured datatype).

AnnotationAssertion
IRI: OntoDM_785336
literal: supervised structure based primitive output prediction task

AnnotationAssertion
IRI: OntoDM_793930
literal: OntoDM_793930s

AnnotationAssertion
IRI: OntoDM_793930
literal: OntoDM_793930

AnnotationAssertion
IRI: OntoDM_793930
literal: LCL tree-based hierarchical classification algorithm is a data mining algorithm that solves a tree-based hierarchical classification task and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_793930
literal: LCL tree based HC algorithm

AnnotationAssertion
IRI: OntoDM_794067
literal: OntoDM_794067s

AnnotationAssertion
IRI: OntoDM_794067
literal: OntoDM_794067

AnnotationAssertion
IRI: OntoDM_794067
literal: feature-based time series predicton dataset is a dataset specification of a labeled dataset with tuple of primitives datatype (specified with the feature set specification) on the descriptive part and a sequence of reals datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_794067
literal: feature-based time-series prediction dataset

AnnotationAssertion
IRI: OntoDM_800750
literal: OntoDM_800750

AnnotationAssertion
IRI: OntoDM_800750
literal: output specification

AnnotationAssertion
IRI: OntoDM_804811
literal: OntoDM_804811

AnnotationAssertion
IRI: OntoDM_804811
literal: integer datatype

AnnotationAssertion
IRI: OntoDM_813772
literal: OntoDM_813772s

AnnotationAssertion
IRI: OntoDM_813772
literal: OntoDM_813772

AnnotationAssertion
IRI: OntoDM_813772
literal: language of bayesian nets

AnnotationAssertion
IRI: OntoDM_815320
literal: OntoDM_815320s

AnnotationAssertion
IRI: OntoDM_815320
literal: OntoDM_815320

AnnotationAssertion
IRI: OntoDM_815320
literal: Multi-target multi-class classification task is a multi-target classification task where the objective is to predict a set of discrete features (represented with a output tuple of discrete ) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_815320
literal: supervised multi-target multi-class classification task

AnnotationAssertion
IRI: OntoDM_817028
literal: OntoDM_817028s

AnnotationAssertion
IRI: OntoDM_817028
literal: OntoDM_817028

AnnotationAssertion
IRI: OntoDM_817028
literal: Multi-target predictive modeling algorithm is a data mining algorithm that solves a multi-target prediction task and as a result produces a multi-target predictive model.

AnnotationAssertion
IRI: OntoDM_817028
literal: multi-target predictive modelling algorithm

AnnotationAssertion
IRI: OntoDM_817888
literal: OntoDM_817888s

AnnotationAssertion
IRI: OntoDM_817888
literal: OntoDM_817888

AnnotationAssertion
IRI: OntoDM_817888
literal: data mining workflow

AnnotationAssertion
IRI: OntoDM_818411
literal: OntoDM_818411s

AnnotationAssertion
IRI: OntoDM_818411
literal: OntoDM_818411

AnnotationAssertion
IRI: OntoDM_818411
literal: GC DAG based HC algorithm for single path and mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_826903
literal: OntoDM_826903s

AnnotationAssertion
IRI: OntoDM_826903
literal: OntoDM_826903

AnnotationAssertion
IRI: OntoDM_826903
literal: Tree based hierarchical classification task with multiple label paths is a tree based hierarchical classification task where the objective is to predict a set of labels organized in a tree (represented with a output tree with boolean edges and discrete nodes) from a set of features (represented by a descriptive tuple of primitives datatype), where the data instances are allowed to have multiple paths of labels.

AnnotationAssertion
IRI: OntoDM_826903
literal: tree-based hierarchical classification task with multiple label paths

AnnotationAssertion
IRI: OntoDM_829270
literal: OntoDM_829270s

AnnotationAssertion
IRI: OntoDM_829270
literal: OntoDM_829270

AnnotationAssertion
IRI: OntoDM_829270
literal: LCPN DAG-based hierarchical classification algorithm for multiple path and non-mandatory leaf node prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with multiple label paths and partial depth labeling and as a result produces a hierarchical classification model using a local classifier per parent node (LCPN) approach.

AnnotationAssertion
IRI: OntoDM_829270
literal: LCPN DAG based HC algorithm for single path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_831808
literal: OntoDM_831808s

AnnotationAssertion
IRI: OntoDM_831808
literal: OntoDM_831808

AnnotationAssertion
IRI: OntoDM_831808
literal: Feature-based predictive modeling algorithm for structured output is a data mining algorithm that solves a feature-based primitive structured prediction task and as a result produces a feature-based predictive model for structured output.

AnnotationAssertion
IRI: OntoDM_831808
literal: feature-based predictive modeling algorithm for structured output

AnnotationAssertion
IRI: OntoDM_835948
literal: OntoDM_835948s

AnnotationAssertion
IRI: OntoDM_835948
literal: OntoDM_835948

AnnotationAssertion
IRI: OntoDM_835948
literal: output structured specification is an output datatype specification that denotes a structured datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_835948
literal: structure-based output data specification

AnnotationAssertion
IRI: OntoDM_837886
literal: OntoDM_837886s

AnnotationAssertion
IRI: OntoDM_837886
literal: OntoDM_837886

AnnotationAssertion
IRI: OntoDM_837886
literal: regression classification task is a feature-based primitive output task where the objective is to predict a real output (represented with a real datatype) from a set of features (represented by a descriptive tuple of primitives datatype).

AnnotationAssertion
IRI: OntoDM_837886
literal: supervised regression task

AnnotationAssertion
IRI: OntoDM_838789
literal: OntoDM_838789s

AnnotationAssertion
IRI: OntoDM_838789
literal: OntoDM_838789

AnnotationAssertion
IRI: OntoDM_838789
literal: LCL DAG-based hierarchical classification algorithm for single path prediction is a data mining algorithm that solves a DAG-based hierarchical classification task with single label paths and as a result produces a hierarchical classification model using a local classifier per level (LCL) approach.

AnnotationAssertion
IRI: OntoDM_838789
literal: LCL DAG based HC algorithm for single path prediction

AnnotationAssertion
IRI: OntoDM_843203
literal: OntoDM_843203s

AnnotationAssertion
IRI: OntoDM_843203
literal: OntoDM_843203

AnnotationAssertion
IRI: OntoDM_843203
literal: labeled structure-based dataset with primitive output is a dataset specification of a labeled dataset with a structure datatype on the descriptive part and a primitive datatype on the output part of the data (specified with the output feature specification).

AnnotationAssertion
IRI: OntoDM_843203
literal: structure-based completely labeled dataset with primitive output

AnnotationAssertion
IRI: OntoDM_843647
literal: OntoDM_843647s

AnnotationAssertion
IRI: OntoDM_843647
literal: OntoDM_843647

AnnotationAssertion
IRI: OntoDM_843647
literal: output sequence of real specification is an output sequence datatype specification that denotes a sequence of reals datatype on the output part of the data.

AnnotationAssertion
IRI: OntoDM_843647
literal: output:sequence of reals

AnnotationAssertion
IRI: OntoDM_849533
literal: OntoDM_849533s

AnnotationAssertion
IRI: OntoDM_849533
literal: OntoDM_849533

AnnotationAssertion
IRI: OntoDM_849533
literal: labeled dataset with primitive output is a dataset specification of labeled dataset with primitive output datatype specification.

AnnotationAssertion
IRI: OntoDM_849533
literal: completely labeled dataset with primitive output

AnnotationAssertion
IRI: OntoDM_849657
literal: OntoDM_849657s

AnnotationAssertion
IRI: OntoDM_849657
literal: OntoDM_849657

AnnotationAssertion
IRI: OntoDM_849657
literal: GC DAG based HC algorithm for single path and non-mandatory leaf node prediction

AnnotationAssertion
IRI: OntoDM_860244
literal: OntoDM_860244s

AnnotationAssertion
IRI: OntoDM_860244
literal: OntoDM_860244

AnnotationAssertion
IRI: OntoDM_860244

AnnotationAssertion
IRI: OntoDM_860244
literal: dataset quality

AnnotationAssertion
IRI: OntoDM_860584
literal: OntoDM_860584s

AnnotationAssertion
IRI: OntoDM_860584
literal: OntoDM_860584