Sequential Algorithm for the Design of Piecewise Linear Classifiers

Abstract

A sequential algorithm for designing piecewise linear classification functions without a priori knowledge of pattern class distributions is described. The algorithm combines adaptive error correcting linear classifier design procedures and clustering techniques under control of a performance criterion. The classification function structure is constrained to minimize design calculations and increase recognition through-put for many classification problems. Examples from the literature are used to evaluate this approach relative to other classification algorithms.