A New Learning Formulation for Kernel Classifier Design

This paper presents a new learning formulation for classifier design called ``General Loss Minimization.´´ The formulation is based on Bayes decision theory which can handle various losses as well as prior probabilities. A learning method for RBF kernel classifiers is derived based on the formulation. Experimental results reveal that the classification accuracy by the proposed method is almost the same as or better than Support Vector Machine (SVM), while the number of obtained reference vectors by the proposed method is much less than that of support vectors by SVM.