Further results on optimization of recognition time

Previous studies on optimization of computational time in pattern recognizers started from strong hypotheses of separability of the classes by a known recognizer and from consideration of naive algorithms which implement the recognizer. Here, we consider weaker separability hypotheses, which allow for doubtful cases, and slightly more sophisticated algorithms. The expressions of the mean computational length and of its total variation actually valid are presented with their relation to the old ones. We give evidence for the fact that the old criterion for deciding about the optimality of an algorithm, by simple ordering of the class-probabilities, is still applicable in this new setting.