Kernel Selection for the Support Vector Machine

被引用文献3件 参考文献15件
  • DEBNATH Rameswar
    Department of Information and Communication Engineering, The University of Electro-Communications
  • TAKAHASHI Haruhisa
    Department of Information and Communication Engineering, The University of Electro-Communications

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説明

The choice of kernel is an important issue in the support vector machine algorithm, and the performance of it largely depends on the kernel. Up to now, no general rule is available as to which kernel should be used. In this paper we investigate two kernels: Gaussian RBF kernel and polynomial kernel. So far Gaussian RBF kernel is the best choice for practical applications. This paper shows that the polynomial kernel in the normalized feature space behaves better or as good as Gaussian RBF kernel. The polynomial kernel in the normalized feature space is the best alternative to Gaussian RBF kernel.

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詳細情報 詳細情報について

  • CRID
    1571980077394206080
  • NII論文ID
    110003213904
  • NII書誌ID
    AA10826272
  • ISSN
    09168532
  • 本文言語コード
    en
  • データソース種別
    • CiNii Articles

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