Kernel Selection for the Support Vector Machine

3 Citations 15 References
  • 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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Description

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.

Journal

  • IEICE Trans. Inf. & Syst., D

    IEICE Trans. Inf. & Syst., D 87 (12), 2903-2904, 2004-12-01

    The Institute of Electronics, Information and Communication Engineers

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Keywords

Details 詳細情報について

  • CRID
    1571980077394206080
  • NII Article ID
    110003213904
  • NII Book ID
    AA10826272
  • ISSN
    09168532
  • Text Lang
    en
  • Data Source
    • CiNii Articles

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