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Optimization of neural classifiers based on bayesian decision boundaries and idle neurons pruning

dc.contributor.authorSilvestre, Miriam Rodrigues [UNESP]
dc.contributor.authorLing, Lee Luan
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2014-05-27T11:20:32Z
dc.date.available2014-05-27T11:20:32Z
dc.date.issued2002-12-01
dc.description.abstractIn this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that realy contribute to correct classification. Also in this article we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method. © 2002 IEEE.en
dc.description.affiliationDep. Matematica-FCT-UNESP
dc.description.affiliationDECOM-FEEC-UNICAMP
dc.description.affiliationUnespDep. Matematica-FCT-UNESP
dc.format.extent387-390
dc.identifierhttp://dx.doi.org/10.1109/ICPR.2002.1047927
dc.identifier.citationProceedings - International Conference on Pattern Recognition, v. 16, n. 3, p. 387-390, 2002.
dc.identifier.doi10.1109/ICPR.2002.1047927
dc.identifier.issn1051-4651
dc.identifier.lattes3356686459975471
dc.identifier.scopus2-s2.0-33751575303
dc.identifier.urihttp://hdl.handle.net/11449/67053
dc.identifier.wosWOS:000177887100094
dc.language.isoeng
dc.relation.ispartofProceedings - International Conference on Pattern Recognition
dc.relation.ispartofsjr0,307
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectBayesian decision boundaries
dc.subjectNeurons
dc.subjectPruning techniques
dc.subjectAlgorithms
dc.subjectDecision theory
dc.subjectMathematical models
dc.subjectNeural networks
dc.subjectPattern recognition
dc.titleOptimization of neural classifiers based on bayesian decision boundaries and idle neurons pruningen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
unesp.author.lattes3356686459975471
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências e Tecnologia, Presidente Prudentept
unesp.departmentMatemática e Computação - FCTpt

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