Applying the Semisupervised Bayesian Approach to Classifier Design

Abstract

This paper adopts a Bayesian approach to learn an optimal nonlinear classifier that is relevant to the classification task of semisupervised problems. The approach uses a prior weight to emphasize on the importance of class, which acts as a parameter of the likelihood function for both labeled and unlabeled data. We derive an expectation-maximization (EM) algorithm to compute maximum likelihood point estimate. Experimental results demonstrate appropriate classification accuracy on both synthetic and benchmark data sets