Support vector machine classifiers for asymmetric proximities

Abstract

The aim of this paper is to afford classification tasks on asymmetric kernel matrices using Support Vector Machines (SVMs). Ordinary theory for SVMs requires to work with symmetric proximity matrices. In this work we examine the performance of several symmetrization methods in classification tasks. In addition we propose a new method that specifically takes classification labels into account to build the proximity matrix. The performance of the considered method is evaluated on a variety of artificial and real data sets. © Springer-Verlag Berlin Heidelberg 2003.