NDLI: Training a one-dimentional classifier to minimize the probability of error

Content Provider	IEEE Xplore Digital Library
Author	Wassel, G.N. Sklansky, J.
Copyright Year	1971
Description	Author affiliation: University of California, Irvine, California (Wassel, G.N.; Sklansky, J.)
Abstract	We report some of the results of a study of asymptotically optimum nonparametric training procedures for two-category pattern classifiers. The decision surfaces yielded by earlier forms of nonparametric training procedures generally do not minimize the probability of error. We derive a modification of the Robbins-Monro method of stochastic approximation, and show how this modification leads to training procedures that minimize the probability of error of a one-dimensional two-category pattern classifier. The class of probability density functions admitted by these training procedures is quite broad, permitting a combination of continuous and discrete components in the density functions. We show that the sequence of decision points generated by any of these training procedures converges with probability one to the minimum-probability-of-error decision point.
Starting Page	332
Ending Page	336
File Size	385999
Page Count	5
File Format	PDF
DOI	10.1109/CDC.1971.271008
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1971-12-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Costs Probability density function Density functional theory
Content Type	Text
Resource Type	Article

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