Information criteria for support vector machines

Kei Kobayashi, Fumiyasu Komaki

研究成果: Article

13 引用 (Scopus)

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This paper presents kernel regularization information criterion (KRIC), which is a new criterion for tuning regularization parameters in kernel logistic regression (KLR) and support vector machines (SVMs). The main idea of the KRIC is based on the regularization information criterion (RIC). We derive an eigenvalue equation to calculate the KRIC and solve the problem. The computational cost for parameter tuning by the KRIC is reduced drastically by using the Nyström approximation. The test error rate of SVMs or KLR with the regularization parameter tuned by the KRIC is comparable with the one by the cross validation or evaluation of the evidence. The computational cost of the KRIC is significantly lower than the one of the other criteria.

元の言語English
ページ(範囲)571-577
ページ数7
ジャーナルIEEE Transactions on Neural Networks
17
発行部数3
DOI
出版物ステータスPublished - 2006 5 1

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ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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