An upper bound on the probability of misclassification in terms of Matusita's measure of affinity

Binay K. Bhattacharya; Godfried T. Toussaint

doi:10.1007/BF02481018

An upper bound on the probability of misclassification in terms of Matusita's measure of affinity

Binay K. Bhattacharya, Godfried T. Toussaint

Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

A distribution-free upper bound is derived on the Bayes probability of misclassification in terms of Matusita's measure of affinity among several distributions for the M-hypothesis discrimination problem. It is shown that the bound is as sharp as possible.

Original language	English (US)
Pages (from-to)	161-165
Number of pages	5
Journal	Annals of the Institute of Statistical Mathematics
Volume	34
Issue number	1
DOIs	https://doi.org/10.1007/BF02481018
State	Published - Dec 1982

Keywords

3.36
3.63
5.25
5.30
5.5
Bhattacharyya coefficient
Matusita's measure of affinity
Probability of misclassification
decision theory
discrimination rules
information measures
pattern classification

ASJC Scopus subject areas

Statistics and Probability

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10.1007/BF02481018

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title = "An upper bound on the probability of misclassification in terms of Matusita's measure of affinity",

abstract = "A distribution-free upper bound is derived on the Bayes probability of misclassification in terms of Matusita's measure of affinity among several distributions for the M-hypothesis discrimination problem. It is shown that the bound is as sharp as possible.",

keywords = "3.36, 3.63, 5.25, 5.30, 5.5, Bhattacharyya coefficient, Matusita's measure of affinity, Probability of misclassification, decision theory, discrimination rules, information measures, pattern classification",

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year = "1982",

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AU - Bhattacharya, Binay K.

AU - Toussaint, Godfried T.

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N2 - A distribution-free upper bound is derived on the Bayes probability of misclassification in terms of Matusita's measure of affinity among several distributions for the M-hypothesis discrimination problem. It is shown that the bound is as sharp as possible.

AB - A distribution-free upper bound is derived on the Bayes probability of misclassification in terms of Matusita's measure of affinity among several distributions for the M-hypothesis discrimination problem. It is shown that the bound is as sharp as possible.

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KW - 5.25

KW - 5.30

KW - 5.5

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KW - Matusita's measure of affinity

KW - Probability of misclassification

KW - decision theory

KW - discrimination rules

KW - information measures

KW - pattern classification

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An upper bound on the probability of misclassification in terms of Matusita's measure of affinity

Abstract

Keywords

ASJC Scopus subject areas

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