A sequential algorithm for piecewise linear classification functions

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