ON THE MINIMIZATION OF CLASSIFICATION ERROR PROBABILITY IN STATISTICAL PATTERN RECOGNITION.

J. Fritz, L. Gyorfi

Research output: Article

Abstract

A two class pattern recognition algorithm is introduced for learning the optimal separating function within a given finite dimensional linear space of functions. It is proved that the algorithm converges with probability one to the separating function minimizing the probability of misclassification over the given class. This stochastic gradient process is based on asymptotically unbiased estimates of the gradient vector of error probability. This method is proposed. to improve classification rules obtained by other methods.

Original languageEnglish
Pages (from-to)371-382
Number of pages12
JournalProbl Control Inf Theory
Volume5
Issue number4
Publication statusPublished - jan. 1 1976

ASJC Scopus subject areas

  • Engineering(all)

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