Thathachar, MAL and Sastry, PS (1987) Learning Optimal Discriminant Functions through a Cooperative Game of Automata. In: IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, 17 (1). pp. 73-85.
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The problem of learning correct decision rules to minimize the probability of misclassification is a long-standing problem of supervised learning in pattern recognition. The problem of learning such optimal discriminant functions is considered for the class of problems where the statistical properties of the pattern classes are completely unknown. The problem is posed as a game with common payoff played by a team of mutually cooperating learning automata. This essentially results in a probabilistic search through the space of classifiers. The approach is inherently capable of learning discriminant functions that are nonlinear in their parameters also. A learning algorithm is presented for the team and convergence is established. It is proved that the team can obtain the optimal classifier to an arbitrary approximation. Simulation results with a few examples are presented where the team learns the optimal classifier.
|Item Type:||Journal Article|
|Additional Information:||Copyright1987 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering|
|Date Deposited:||31 Aug 2009 08:22|
|Last Modified:||19 Sep 2010 05:38|
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