A TIGHT UPPER BOUND ON THE BAYESIAN PROBABILITY OF ERROR

被引：20

作者：

HASHLAMOUN, WA ^{[1
]}

VARSHNEY, PK ^{[1
]}

SAMARASOORIYA, VNS ^{[1
]}

机构：

[1] SYRACUSE UNIV,DEPT ELECT & COMP ENGN,SYRACUSE,NY 13244

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1994年 / 16卷 / 02期

关键词：

ALI-SILVEY DISTANCE MEASURES; BAYESIAN DECISION SYSTEMS; DIVERGENCE; MINIMUM PROBABILITY OF ERROR; PROBABILITY OF ERROR BOUNDS; STATISTICAL PATTERN RECOGNITION;

D O I：

10.1109/34.273728

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present a new upper bound on the minimum probability of error of Bayesian decision systems. This new bound is continuous everywhere and is shown to be tighter than several existing bounds such as the Bhattacharyya and the Bayesian bounds. Numerical results are also presented.

引用

页码：220 / 224

页数：5

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