Pattern Classifier Design by Linear Programming

Abstract

Abstract—A common nonparametric method for designing linear discriminant functions for pattern classification is the iterative, or "adaptive," weight adjustment procedure, which designs the discriminant function to do well on a set of typical patterns. This paper presents a linear programming formulation of discriminant function design which minimizes the same objective function as the "fixed-increment" adaptive method. With this formulation, as with the adaptive methods, weights which tend to minimize the number of classification errors are computed for both separable and nonseparable pattern sets, and not just for separable pattern sets as has been the emphasis in previous linear programming formulations.