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Game Theoretical Pattern Recognition: Application to Imperfect Noncooperative Learning and to Multiclass Classification
January 1984 (vol. 6 no. 1)
pp. 118-122
L. F. Pau, Battelle Memorial Institute, 7 Route de Drize, CH-1227 Carouge, Switzerland.
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.
Citation:
L. F. Pau, "Game Theoretical Pattern Recognition: Application to Imperfect Noncooperative Learning and to Multiclass Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 1, pp. 118-122, Jan. 1984, doi:10.1109/TPAMI.1984.4767486
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