VC-dimension of rule sets

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dc.authorid0000-0001-5838-4615
dc.contributor.authorYıldız, Olcay Taneren_US
dc.date.accessioned2015-11-24T14:10:51Z
dc.date.available2015-11-24T14:10:51Z
dc.date.issued2014-12-04
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 lower bounds of the VC-dimension of the rule set hypothesis class where the input features are binary or continuous. The VC-dimension of the rule set depends on the VC-dimension values of its rules and the number of inputs.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationYıldız, O. T. (2014). VC-dimension of rule sets. Paper presented at the International Conference on Pattern Recognition, 3576-3581. doi:10.1109/ICPR.2014.615en_US
dc.identifier.doi10.1109/ICPR.2014.615
dc.identifier.endpage3581
dc.identifier.isbn9781479952083
dc.identifier.issn1051-4651
dc.identifier.scopus2-s2.0-84919935805
dc.identifier.scopusqualityN/A
dc.identifier.startpage3576
dc.identifier.urihttps://hdl.handle.net/11729/720
dc.identifier.urihttp://dx.doi.org/10.1109/ICPR.2014.615
dc.identifier.wosWOS:000359818003119
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakConference Proceedings Citation Index – Science (CPCI-S)en_US
dc.institutionauthorYıldız, Olcay Taneren_US
dc.institutionauthorid0000-0001-5838-4615
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEE Computer Socen_US
dc.relation.ispartofInternational Conference on Pattern Recognitionen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectVC-Dimensionen_US
dc.subjectRule Setsen_US
dc.subjectComputersen_US
dc.subjectDecision treesen_US
dc.subjectLabelingen_US
dc.subjectPattern recognitionen_US
dc.subjectStatistical learningen_US
dc.subjectTrainingen_US
dc.subjectVectorsen_US
dc.subjectLearning (artificial intelligence)en_US
dc.subjectSet theoryen_US
dc.subjectVC-dimension valuesen_US
dc.subjectBinary input featuresen_US
dc.subjectContinuous input featuresen_US
dc.subjectLower boundsen_US
dc.subjectRule set hypothesis classen_US
dc.titleVC-dimension of rule setsen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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