An upper bound on the probability of misclassification in terms of the affinity

1 January 1977

journal article
Published by Institute of Electrical and Electronics Engineers (IEEE) in Proceedings of the IEEE

Vol. 65 (2) , 275-276
https://doi.org/10.1109/PROC.1977.10469

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.

Keywords

UPPER BOUND
ERROR ANALYSIS
FILTERS
RADIO FREQUENCY
RADIOFREQUENCY INTERFERENCE
PHASE SHIFT KEYING
PHASE NOISE
SIGNAL TO NOISE RATIO
QUADRATURE PHASE SHIFT KEYING
DIGITAL COMMUNICATION

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