Learning automata algorithms for pattern classification

Abstract

This paper considers the problem of learning optimal discriminant functions for pattern classification. The criterion of optimality is minimising the probability of misclassification. No knowledge of the statistics of the pattern classes is assumed and the given classified sample may be noisy. We present a comprehensive review of algorithms based on the model of cooperating systems of learning automata for this problem. Both finite action set automata and continuous action set automata models are considered. All algorithms presented have rigorous convergence proofs. We also present algorithms that converge to global optimum. Simulation results are presented to illustrate the effectiveness of these techniques based on learning automata.