Rajaraman, K and Sastry, PS (1999) Stochastic Optimization Over Continuous and Discrete Variables with Applications to Concept Learning Under Noise. In: IEEE Transactions on Systems Man and Cybernetics Part A Systems and Humans, 29 (6). pp. 542-553.
We consider optimization problems where the objective function is defined over some continuous and some discrete variables, and only noise corrupted values of the objective function are observable, Such optimization problems occur naturally in PAC learning with noisy samples, We propose a stochastic learning algorithm based on the model of a hybrid team of learning automata involved in a stochastic game with incomplete information to solve this optimization problem and establish its convergence properties, We then illustrate an application of this automata model in learning a class of conjunctive logic expressions over both nominal and linear attributes under noise.
|Item Type:||Journal Article|
|Additional Information:||©1999 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.|
|Keywords:||Concept learning;learning automata;ODE analysis of learning algorithms;optimization;PAC learning;risk minimization|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering|
|Date Deposited:||25 Aug 2008|
|Last Modified:||19 Sep 2010 04:15|
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