Bayesian trigonometric support vector classifier

Authors:
Wei Chu;S. Sathiya Keerthi;Chong Jin Ong
Affiliations:
Department of Mechanical Engineering, National University of Singapore, Singapore, 119260;Department of Mechanical Engineering, National University of Singapore, Singapore, 119260;Department of Mechanical Engineering, National University of Singapore, Singapore, 119260
Venue:
Neural Computation
Year:
2003

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Abstract

This letter describes Bayesian techniques for support vector classification. In particular, we propose a novel differentiable loss function, called the trigonometric loss function, which has the desirable characteristic of natural normalization in the likelihood function, and then follow standard gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implement model adaptation, while keeping the merits of support vector classifier, such as sparseness and convex programming. This differs from standard gaussian processes for classification. Moreover, we put forward class probability in making predictions. Experimental results on benchmark data sets indicate the usefulness of this approach.