The property of the Kullback-Leibler Kernel for Normalized Frequency Spectrum

DOI
  • ISHIGAKI Tsukasa
    Department of Statistical Science, Graduate School of Multidisciplinary Sciences, The Graduate University for Advanced Studies
  • HIGUCHI Tomoyuki
    The Institute of Statistical Mathematics

Bibliographic Information

Other Title
  • Kullback-Leibler カーネルの正規化スペクトル判別における特性

Abstract

<p>The kernel classifier that realizes a nonlinear classification such as Support Vector Machine has been successfully implemented in a number of fields. In the kernel method, the appropriate selection or design of the kernel function is important for the construction of a classifier that has high performance. The present paper describes a normalized frequency spectrum classification method using the SVM with the Kullback-Leibler (KL) kernel. We introduce the KL kernel to normalized spectrum classification and study the property of similarity calculation of the KL kernel and other common kernels with respect to the change in the appearance position of spectrum peaks.</p>

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Details 詳細情報について

  • CRID
    1390289225020714112
  • NII Article ID
    130008079488
  • DOI
    10.11517/jsaisigtwo.2007.dmsm-a701_15
  • ISSN
    24365556
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
    • KAKEN
  • Abstract License Flag
    Allowed

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