Game theoretical pattern recognition: application to imperfect noncooperative learning and to multiclass classification.

This paper studies in theory, and gives a solution to, the following concerns which may eventually be simultaneous: 1) obtain alternative classification decisions, ranked by some decreasing order of class membership probabilities; 2) imperfect teacher at the learning stage, or effects of labeling errors due to unsupervised learning by clustering; 3) noncooperative teacher, manipulating the a priori class probabilities; 4) unknown a priori class probabilities. These requirements are taken into account by considering a game between the recognition system and the teacher, in a game theoretical framework. Both players will ultimately select ``mixed strategies,'' which are probability distributions over the set of N alternative pattern classes, determined for each feature vector to be classified. This solution concept is interpreted in terms of the requirements 1)-4); numerical algorithms, as well as numerical examples are given with their solutions.