Bayesian Inference in Trigonometric Support Vector Classifier

In this paper, we apply popular Bayesian techniques on support vector classifier. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristics of natural normalization in the likelihood function, and describe a Bayesian framework in stationary Gaussian stochastic processes. 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. Moreover, we put forward class probability in making predictions. Experimental results on benchmark data sets indicate the feasibility of this approach even on large data sets.