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http://acervodigital.unesp.br/handle/11449/113508
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DC Field | Value | Language |
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dc.contributor.author | Iwashita, A. S. | - |
dc.contributor.author | Papa, João Paulo | - |
dc.contributor.author | Souza, A. N. | - |
dc.contributor.author | Falcao, A. X. | - |
dc.contributor.author | Lotufo, R. A. | - |
dc.contributor.author | Oliveira, V. M. | - |
dc.contributor.author | Albuquerque, Victor Hugo C. de | - |
dc.contributor.author | Tavares, Joao Manuel R. S. | - |
dc.date.accessioned | 2014-12-03T13:11:45Z | - |
dc.date.accessioned | 2016-10-25T20:15:03Z | - |
dc.date.available | 2014-12-03T13:11:45Z | - |
dc.date.available | 2016-10-25T20:15:03Z | - |
dc.date.issued | 2014-04-15 | - |
dc.identifier | http://dx.doi.org/10.1016/j.patrec.2013.12.018 | - |
dc.identifier.citation | Pattern Recognition Letters. Amsterdam: Elsevier Science Bv, v. 40, p. 121-127, 2014. | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.uri | http://hdl.handle.net/11449/113508 | - |
dc.identifier.uri | http://acervodigital.unesp.br/handle/11449/113508 | - |
dc.description.abstract | In 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.sponsorship | Fundacao para a Ciencia e a Tecnologia (FCT) in Portugal | - |
dc.format.extent | 121-127 | - |
dc.language.iso | eng | - |
dc.publisher | Elsevier B.V. | - |
dc.source | Web of Science | - |
dc.subject | Machine learning | en |
dc.subject | Pattern recognition | en |
dc.subject | Optimum-path forest | en |
dc.title | A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier | en |
dc.type | outro | - |
dc.contributor.institution | Universidade Estadual Paulista (UNESP) | - |
dc.contributor.institution | Universidade Estadual de Campinas (UNICAMP) | - |
dc.contributor.institution | Univ Fortaleza | - |
dc.contributor.institution | Univ Porto | - |
dc.description.affiliation | Unesp Univ Estadual Paulista, Dept Comp, BR-17033360 Bauru, Brazil | - |
dc.description.affiliation | Unesp Univ Estadual Paulista, Dept Engn Eletr, BR-17033360 Bauru, Brazil | - |
dc.description.affiliation | Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP, Brazil | - |
dc.description.affiliation | Univ Estadual Campinas, Fac Eng Eletr & Comp, BR-13083852 Campinas, SP, Brazil | - |
dc.description.affiliation | Univ Fortaleza, Programa Posgrad Informat Aplicada, BR-60811905 Fortaleza, Ceara, Brazil | - |
dc.description.affiliation | Univ Porto, Fac Engn, Dept Eng Mecan, Inst Eng Mecan & Gestao Ind, P-4200465 Oporto, Portugal | - |
dc.description.affiliationUnesp | Unesp Univ Estadual Paulista, Dept Comp, BR-17033360 Bauru, Brazil | - |
dc.description.affiliationUnesp | Unesp Univ Estadual Paulista, Dept Engn Eletr, BR-17033360 Bauru, Brazil | - |
dc.description.sponsorshipId | Fundacao para a Ciencia e a Tecnologia (FCT) in PortugalPTDC/BBB-BMD/3088/2012 | - |
dc.identifier.doi | 10.1016/j.patrec.2013.12.018 | - |
dc.identifier.wos | WOS:000333105600016 | - |
dc.rights.accessRights | Acesso restrito | - |
dc.relation.ispartof | Pattern Recognition Letters | - |
Appears in Collections: | Artigos, TCCs, Teses e Dissertações da Unesp |
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