Multiple Kernel Learning for Quadratically Constrained MAP Classification

• WASHIZAWA Yoshikazu

  The Univeristy of Electro-Communications Brain Science Institute, RIKEN

• YOKOTA Tatsuya

  Brain Science Institute, RIKEN Tokyo Institute of Technology

• YAMASHITA Yukihiko

  Tokyo Institute of Technology

抄録

Most of the recent classification methods require tuning of the hyper-parameters, such as the kernel function parameter and the regularization parameter. Cross-validation or the leave-one-out method is often used for the tuning, however their computational costs are much higher than that of obtaining a classifier. Quadratically constrained maximum a posteriori (QCMAP) classifiers, which are based on the Bayes classification rule, do not have the regularization parameter, and exhibit higher classification accuracy than support vector machine (SVM). In this paper, we propose a multiple kernel learning (MKL) for QCMAP to tune the kernel parameter automatically and improve the classification performance. By introducing MKL, QCMAP has no parameter to be tuned. Experiments show that the proposed classifier has comparable or higher classification performance than conventional MKL classifiers.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E97.D (5), 1340-1344, 2014

  一般社団法人 電子情報通信学会

キーワード

• quadratically constrained MAP
• multiple kernel learning
• support vector machine
• Bayes classification rule

詳細情報

• CRID: 1390282679356317312
• NII論文ID: 130004519249
• DOI: 10.1587/transinf.e97.d.1340
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E97.D/5/E97.D_1340/_pdf
• 本文言語コード: en
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