Learning kernels for support vector machines with polynomial powers of sigmoid
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Undergraduate course
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Ieee
Type
Work presented at event
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Acesso aberto
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Abstract
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.
Description
Keywords
Machine learning, Kernel functions, Polynomial powers of sigmoid, PPS-Radial, Support vector machines
Language
English
Citation
2014 27th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 259-265, 2014.
