VC-dimension of univariate decision trees

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dc.authorid0000-0001-5838-4615
dc.contributor.authorYıldız, Olcay Taneren_US
dc.date.accessioned2015-07-14T11:00:06Z
dc.date.available2015-07-14T11:00:06Z
dc.date.issued2015-02-25
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractIn this paper, we give and prove the lower bounds of the Vapnik-Chervonenkis (VC)-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension values of its subtrees and the number of inputs. Via a search algorithm that calculates the VC-dimension of univariate decision trees exhaustively, we show that our VC-dimension bounds are tight for simple trees. To verify that the VC-dimension bounds are useful, we also use them to get VC-generalization bounds for complexity control using structural risk minimization in decision trees, i.e., pruning. Our simulation results show that structural risk minimization pruning using the VC-dimension bounds finds trees that are more accurate as those pruned using cross validation.en_US
dc.description.versionPublisher's Versionen_US
dc.description.versionAuthor Post Printen_US
dc.identifier.citationYıldız, O. T. (2015). VC-dimension of univariate decision trees. IEEE Transactions on Neural Networks and Learning Systems, 26(2), 378-387. doi:10.1109/TNNLS.2014.2385837en_US
dc.identifier.doi10.1109/TNNLS.2014.2385837
dc.identifier.endpage387
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.issue2
dc.identifier.pmid25594983
dc.identifier.scopus2-s2.0-84921446924
dc.identifier.scopusqualityN/A
dc.identifier.startpage378
dc.identifier.urihttps://hdl.handle.net/11729/581
dc.identifier.urihttp://dx.doi.org/10.1109/TNNLS.2014.2385837
dc.identifier.volume26
dc.identifier.wosWOS:000348856200015
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorYıldız, Olcay Taneren_US
dc.institutionauthorid0000-0001-5838-4615
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEE-INST Electrical Electronics Engineers Incen_US
dc.relation.journalIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputation theoryen_US
dc.subjectDecision treesen_US
dc.subjectLearningen_US
dc.subjectMachine learningen_US
dc.subjectSupervised learningen_US
dc.subjectVapnik-Chervonenkis (VC)-dimensionen_US
dc.subjectModel selectionen_US
dc.subjectClassifiersen_US
dc.subjectRegressionen_US
dc.subjectComplexityen_US
dc.subjectBoundsen_US
dc.titleVC-dimension of univariate decision treesen_US
dc.typeArticleen_US
dspace.entity.typePublication

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