Learning Optimal Conjunctive Concepts through a Team of Stochastic Automata

Sastry, PS and Rajaraman, K and Ranjan, SR (1993) Learning Optimal Conjunctive Concepts through a Team of Stochastic Automata. In: IEEE Transactions on Systems,Man and Cybernetics, 23 (4). 1175 -1184.

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Abstract

The problem of learning conjunctive concepts from a series of positive and negative examples of the concept is considered. Employing a probabilistic structure on the domain, the goal of such inductive learning is precisely characterized. A parallel distributed stochastic algorithm is presented. It is proved that the algorithm will converge to the concept description with maximum probability of correct classification in the pres ence of up to 50% unbiased noise. A novel neural network structure that implements the learning algorithm is proposed. Through empirical studies it is seen that the algorithm is quite efficient for learning conjunctive concepts.

Item Type:	Journal Article
Publication:	IEEE Transactions on Systems,Man and Cybernetics
Publisher:	IEEE
Additional Information:	Copyright 1990 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:	22 Aug 2008
Last Modified:	19 Sep 2010 04:26
URI:	http://eprints.iisc.ac.in/id/eprint/6799

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