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An Adaptive Scheme for Learning the Probability Threshold in Pattern Recognition

Dattatreya, GR and Sarma, VVS (1982) An Adaptive Scheme for Learning the Probability Threshold in Pattern Recognition. In: IEEE Trans Syst Man Cybern Syst Hum, 12 (6). pp. 927-934.

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Abstract

The statistical minimum risk pattern recognition problem, when the classification costs are random variables of unknown statistics, is considered. Using medical diagnosis as a possible application, the problem of learning the optimal decision scheme is studied for a two-class twoaction case, as a first step. This reduces to the problem of learning the optimum threshold (for taking appropriate action) on the a posteriori probability of one class. A recursive procedure for updating an estimate of the threshold is proposed. The estimation procedure does not require the knowledge of actual class labels of the sample patterns in the design set. The adaptive scheme of using the present threshold estimate for taking action on the next sample is shown to converge, in probability, to the optimum. The results of a computer simulation study of three learning schemes demonstrate the theoretically predictable salient features of the adaptive scheme.

Item Type: Journal Article
Additional Information: Copyright 1982 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 > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 10 Jul 2009 10:15
Last Modified: 19 Sep 2010 05:35
URI: http://eprints.iisc.ernet.in/id/eprint/21093

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