Multiple Kernel Learning for Quadratically Constrained MAP Classification

Yoshikazu WASHIZAWA  Tatsuya YOKOTA  Yukihiko YAMASHITA  

IEICE TRANSACTIONS on Information and Systems   Vol.E97-D    No.5    pp.1340-1344
Publication Date: 2014/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E97.D.1340
Type of Manuscript: LETTER
Category: Fundamentals of Information Systems
quadratically constrained MAP,  multiple kernel learning,  support vector machine,  Bayes classification rule,  

Full Text: PDF>>
Buy this Article

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.