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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/130175
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dc.contributor.authorFernandes, Silas E. N.-
dc.contributor.authorPilastri, Andre Luiz-
dc.contributor.authorPereira, Luis A. M.-
dc.contributor.authorPires, Rafael G.-
dc.contributor.authorPapa, João P.-
dc.date.accessioned2015-11-03T15:29:57Z-
dc.date.accessioned2016-10-25T21:17:23Z-
dc.date.available2015-11-03T15:29:57Z-
dc.date.available2016-10-25T21:17:23Z-
dc.date.issued2014-01-01-
dc.identifierhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6915316-
dc.identifier.citation2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 259-265, 2014.-
dc.identifier.urihttp://hdl.handle.net/11449/130175-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/130175-
dc.description.abstractIn the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.en
dc.format.extent259-265-
dc.language.isoeng-
dc.publisherIeee-
dc.sourceWeb of Science-
dc.subjectMachine learningen
dc.subjectKernel functionsen
dc.subjectPolynomial powers of sigmoiden
dc.subjectPPS-Radialen
dc.subjectSupport vector machinesen
dc.titleLearning kernels for support vector machines with polynomial powers of sigmoiden
dc.typeoutro-
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)-
dc.contributor.institutionUniversidade do Estado de Mato Grosso (UNEMAT)-
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationDepartment of Computing, UFSCar - Federal University of Sao Carlos, São Carlos - SP, Brazil.-
dc.description.affiliationDepartment of Computing, UNEMAT - Univ State of Mato Grosso, Alto Araguaia - MT, Brazil.-
dc.description.affiliationInstitute of Computing, University of Campinas, Campinas - SP, Brazil.-
dc.description.affiliationUnespUniversidade Estadual Paulista, Department of Computing, Bauru - SP, Brazil.-
dc.identifier.doihttp://dx.doi.org/10.1109/SIBGRAPI.2014.36-
dc.identifier.wosWOS:000352613900034-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartof2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi)-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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