dc.creatorSouza, R
dc.creatorRittner, L
dc.creatorLotufo, R
dc.date2014
dc.dateAPR 1
dc.date2014-07-30T13:39:24Z
dc.date2015-11-26T16:35:27Z
dc.date2014-07-30T13:39:24Z
dc.date2015-11-26T16:35:27Z
dc.date.accessioned2018-03-28T23:17:55Z
dc.date.available2018-03-28T23:17:55Z
dc.identifierPattern Recognition Letters. Elsevier Science Bv, v. 39, n. 2, n. 10, 2014.
dc.identifier0167-8655
dc.identifier1872-7344
dc.identifierWOS:000331854700002
dc.identifier10.1016/j.patrec.2013.08.030
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/52986
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/52986
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1271516
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionThis paper presents the k-Optimum Path Forest (k-OPF) supervised classifier, which is a natural extension of the OPF classifier. k-OPF is compared to the k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Decision Tree (DT) classifiers, and we see that k-OPF and k-NN have many similarities. This work shows that the k-OPF is equivalent to the k-NN classifier when all training samples are used as prototypes. Simulations comparing the accuracy results, the decision boundaries and the processing time of the classifiers are presented to experimentally validate our hypothesis. Also, we prove that OPF using the max cost function and the NN supervised classifiers have the same theoretical error bounds. (C) 2013 Elsevier B.V. All rights reserved.
dc.description39
dc.descriptionSI
dc.description2
dc.description10
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationPattern Recognition Letters
dc.relationPattern Recognit. Lett.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectOptimum Path Forest
dc.subjectNearest Neighbors
dc.subjectSupervised classification
dc.subjectPattern-classification
dc.subjectRule
dc.titleA comparison between k-Optimum Path Forest and k-Nearest Neighbors supervised classifiers
dc.typeArtículos de revistas


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