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dc.contributor.authorIwashita, A. S. [UNESP]
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.authorSouza, A. N. [UNESP]
dc.contributor.authorFalcao, A. X.
dc.contributor.authorLotufo, R. A.
dc.contributor.authorOliveira, V. M.
dc.contributor.authorAlbuquerque, Victor Hugo C. de
dc.contributor.authorTavares, Joao Manuel R. S.
dc.date.accessioned2014-12-03T13:11:45Z
dc.date.available2014-12-03T13:11:45Z
dc.date.issued2014-04-15
dc.identifierhttp://dx.doi.org/10.1016/j.patrec.2013.12.018
dc.identifier.citationPattern Recognition Letters. Amsterdam: Elsevier Science Bv, v. 40, p. 121-127, 2014.
dc.identifier.issn0167-8655
dc.identifier.urihttp://hdl.handle.net/11449/113508
dc.description.abstractIn general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.en
dc.description.sponsorshipFundacao para a Ciencia e a Tecnologia (FCT) in Portugal
dc.format.extent121-127
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofPattern Recognition Letters
dc.sourceWeb of Science
dc.subjectMachine learningen
dc.subjectPattern recognitionen
dc.subjectOptimum-path foresten
dc.titleA path- and label-cost propagation approach to speedup the training of the optimum-path forest classifieren
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniv Fortaleza
dc.contributor.institutionUniv Porto
dc.description.affiliationUnesp Univ Estadual Paulista, Dept Comp, BR-17033360 Bauru, Brazil
dc.description.affiliationUnesp Univ Estadual Paulista, Dept Engn Eletr, BR-17033360 Bauru, Brazil
dc.description.affiliationUniv Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP, Brazil
dc.description.affiliationUniv Estadual Campinas, Fac Eng Eletr & Comp, BR-13083852 Campinas, SP, Brazil
dc.description.affiliationUniv Fortaleza, Programa Posgrad Informat Aplicada, BR-60811905 Fortaleza, Ceara, Brazil
dc.description.affiliationUniv Porto, Fac Engn, Dept Eng Mecan, Inst Eng Mecan & Gestao Ind, P-4200465 Oporto, Portugal
dc.description.affiliationUnespUnesp Univ Estadual Paulista, Dept Comp, BR-17033360 Bauru, Brazil
dc.description.affiliationUnespUnesp Univ Estadual Paulista, Dept Engn Eletr, BR-17033360 Bauru, Brazil
dc.identifier.doi10.1016/j.patrec.2013.12.018
dc.identifier.wosWOS:000333105600016
dc.rights.accessRightsAcesso restrito
dc.description.sponsorshipIdFundacao para a Ciencia e a Tecnologia (FCT) in PortugalPTDC/BBB-BMD/3088/2012
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Engenharia, Baurupt
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Ciências, Baurupt
dc.identifier.lattes8212775960494686
unesp.author.lattes8212775960494686
unesp.author.orcid0000-0002-6494-7514[2]
dc.relation.ispartofjcr1.952
dc.relation.ispartofsjr0,662
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