Two variations on Fisher's linear discriminant for pattern recognition

Abstract

Discriminants are often used in pattern recognition to separate clusters of points in some multidimensional "feature" space. The paper provides two fast and simple techniques for improving on the classification performance provided by Fisher's linear discriminant for two classes. Both of these methods are also extended to nonlinear decision surfaces through the use of Mercer kernels.