Publicação: Learning kernels for support vector machines with polynomial powers of sigmoid
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Data
2014-01-01
Orientador
Coorientador
Pós-graduação
Curso de graduação
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Título de Volume
Editor
Ieee
Tipo
Trabalho apresentado em evento
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Acesso aberto
Resumo
In 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.
Descrição
Idioma
Inglês
Como citar
2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 259-265, 2014.
