대한전자공학회:학술대회논문집 (Proceedings of the IEEK Conference)

대한전자공학회 (The Institute of Electronics and Information Engineers)
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On the Support Vector Machine with the kernel of the q-normal distribution

  • Joguchi, Hirofumi (Department of Information & Computer Sciences Saitama University) ;
  • Tanaka, Masaru (Department of Information & Computer Sciences Saitama University)
  • 발행 : 2002.07.01
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초록

Support Vector Machine (SVM) is one of the methods of pattern recognition that separate input data using hyperplane. This method has high capability of pattern recognition by using the technique, which says kernel trick, and the Radial basis function (RBF) kernel is usually used as a kernel function in kernel trick. In this paper we propose using the q-normal distribution to the kernel function, instead of conventional RBF, and compare two types of the kernel function.

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