Recognition rule acquisition by an advanced extension matrix algorithm

Abstract

An advanced Extension Matrix algorithm is proposed, in which a heuristic search based on the average entropy is used to get the approximate solutions of the minimal complex, as well as a potential function is used to estimate the probability density function of the overlay area between positive and negative examples, so that the non-linear interfaces of the interclass areas may be obtained. The algorithm will be applied to supervised learning for Chinese character recognition to acquire recognition rules, and the implementations upon 751,000 loosely-constrained handwritten Chinese characters indicate that these methodologies can be applied to a practical recognition system with promising results.