Fuzzy proximal support vector classification via generalized eigenvalues

Abstract

In this paper, we propose a fuzzy extension to proximal support vector classification via generalized eigenvalues. Here, a fuzzy membership value is assigned to each pattern, and points are classified by assigning them to the nearest of two non parallel planes that are close to their respective classes. The algorithm is simple as the solution requires solving a generalized eigenvalue problem as compared to SVMs, where the classifier is obtained by solving a quadratic programming problem. The approach can be used to obtain an improved classification when one has an estimate of the fuzziness of samples in either class. © Springer-Verlag Berlin Heidelberg 2005.