Support Vector Machine for Nonparametric Binary Hypothesis Testing

摘要

The Support Vector Machine, introduced in [1] as a practical implementation of the principle of structural risk minimization, constitutes one of the most promising methods for constructing a mathematical model only on the base of a limited amount of measured data. In this paper, we consider the application of this method to the problem of nonparamet-ric binary hypothesis testing (bayesian setting); the main contribution of this paper is the derivation of the Support Vector algorithm in the case of a generic convex approximation of the binary risk function.