An Upper Bound on the Probability of Misclassification in Terms of the Affinity

Research output: Contribution to journalArticle

Abstract

A distribution-free upper bound is derived for the Bayes probability of misclassification in terms of Matusita's measure of affinity of several distributions for the multihypothesis pattern recognition problem. It is shown that for the two-class problem the bound reduces to the Hudimoto-Kailath bound in terms of the Bhattacharyya coefficient. An additional upper bound is derived which is independent of the a priori probabilities of the pattern classes.

Original languageEnglish (US)
Pages (from-to)275-276
Number of pages2
JournalProceedings of the IEEE
Volume65
Issue number2
DOIs
StatePublished - Feb 1977

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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