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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authoremailpapa@fc.unesp.brpt_BR
dc.contributor.authorunicampFalcão, Alexandre Xavierpt_BR
dc.typeArtigopt_BR
dc.titleOptimum-path Forest Based On K-connectivity: Theory And Applicationsen
dc.titleOptimum-path forest based on k-connectivity : theory and applicationspt_BR
dc.contributor.authorPapa, João Paulopt_BR
dc.contributor.authorNachif Fernandes, Silas Evandropt_BR
dc.contributor.authorFalcao, Alexandre Xavierpt_BR
unicamp.author[Falcao, Alexandre Xavier] Univ Estadual Campinas, Inst Comp, Av Albert Einstein 1251, BR-13083852 Campinas, SP, Brazilpt_BR
unicamp.author.external[Papa, Joao Paulo] Sao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube, BR-17033360 Bauru, SP, Brazilpt_BR
unicamp.author.external[Nachif Fernandes, Silas Evandro] Univ Fed Sao Carlos, Dept Comp, Rod Washington Luis,Km 235, BR-13565905 Sao Carlos, SP, Brazilpt_BR
dc.subjectPattern Classificationen
dc.subjectOptimum-path Foresten
dc.subjectSupervised Learningen
dc.subjectReconhecimento de padrõespt_BR
dc.subjectFloresta de caminhos ótimospt_BR
dc.subjectAprendizado de máquinapt_BR
dc.subjectInteligência artificialpt_BR
dc.subject.otherlanguagePattern recognitionpt_BR
dc.subject.otherlanguageOptimum-path forestpt_BR
dc.subject.otherlanguageMachine learningpt_BR
dc.subject.otherlanguageArtificial intelligencept_BR
dc.description.abstractGraph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the last years, mainly due to the promising results obtained by OPF-based classifiers, which range from unsupervised, semi-supervised and supervised learning. In this paper, we consider a deeper theoretical explanation concerning the supervised OPF classifier with k-neighborhood (OPFk), as well as we proposed two different training and classification algorithms that allow OPFk to work faster. The experimental validation against standard OPF and Support Vector Machines also validates the robustness of OPFk in real and synthetic datasets. (C) 2016 Elsevier B.V. All rights reserved.en
dc.description.abstractGraph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the laspt_BR
dc.relation.ispartofPattern recognition letterspt_BR
dc.publisher.cityAmsterdampt_BR
dc.publisher.countryPaíses Baixospt_BR
dc.publisherElsevierpt_BR
dc.date.issued2017pt_BR
dc.date.monthofcirculationFeb.pt_BR
dc.identifier.citationPattern Recognition Letters. Elsevier Science Bv, v. 87, p. 117 - 126, 2017.pt_BR
dc.language.isoengpt_BR
dc.description.volume87pt_BR
dc.description.firstpage117pt_BR
dc.description.lastpage126pt_BR
dc.rightsfechadopt_BR
dc.rightsFechadopt_br
dc.sourceWOSpt_BR
dc.identifier.issn0167-8655pt_BR
dc.identifier.eissn1872-7344pt_BR
dc.identifier.wosidWOS:000395616700015pt_BR
dc.identifier.doi10.1016/j.patrec.2016.07.026pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0167865516302057pt_BR
dc.description.sponsorshipCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORpt_BR
dc.description.sponsorshipFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOpt_BR
dc.description.sponsorshipCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOpt_BR
dc.description.sponsorship1Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)pt_BR
dc.description.sponsorship1Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsorship1Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsordocumentnumberPROCAD 2966/2014pt_BR
dc.description.sponsordocumentnumber2009/16206-1; 2013/20387-7; 2014/2014/16250-9pt_BR
dc.description.sponsordocumentnumber303182/2011-3; 70571/2013-6; 306166/2014-3pt_BR
dc.date.available2017-11-13T13:54:51Z-
dc.date.accessioned2017-11-13T13:54:51Z-
dc.description.provenanceMade available in DSpace on 2017-11-13T13:54:51Z (GMT). No. of bitstreams: 1 000395616700015.pdf: 1633820 bytes, checksum: 968f17a3d9e653fd2c38319cc5fa9c44 (MD5) Previous issue date: 2017 Bitstreams deleted on 2021-01-04T14:26:16Z: 000395616700015.pdf,. Added 1 bitstream(s) on 2021-01-04T14:27:20Z : No. of bitstreams: 1 000395616700015.pdf: 1699003 bytes, checksum: f071680466bd209c985158a8f5b8a204 (MD5)en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/329519-
dc.description.conferencenome10th IAPR-TC15 workshop on graph-based representations in pattern recognitionpt_BR
dc.description.conferencedateMAY 13-15, 2015pt_BR
dc.description.conferencelocationBeijing, PEOPLES R CHINApt_BR
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordPattern classificationpt_BR
dc.subject.keywordOptimum-path forestpt_BR
dc.subject.keywordSupervised learningpt_BR
dc.identifier.source000395616700015pt_BR
dc.creator.orcid0000-0002-2914-5380pt_BR
dc.type.formArtigo de eventopt_BR
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