On discretizing estimator-based learning algorithms

The authors illustrate the improvements gained by rendering various estimator algorithms discrete. Experimental results indicate that discretizing improves the performance of estimator algorithms. It is believed that discrete estimator algorithms (DEAs) constitute the fastest converging and most accurate learning automata reported to date. The DEAs are shown to have the monotone and moderation properties. Finally, any discretized learning automaton with monotone and moderation properties is proven to be ε-optimal in all stationary environments