Arbitrarily tight upper and lower bounds on the Bayesian probability of error

被引：32

作者：

AviItzhak, H ^{[1
]}

Diep, T ^{[1
]}

机构：

[1] CANON RES CTR,PALO ALTO,CA 94304

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1996年 / 18卷 / 01期

关键词：

Bayesian decision; probability of error; statistical pattern recognition;

D O I：

10.1109/34.476017

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents new upper and lower bounds on the minimum probability of error of Bayesian decision systems for the two-class problem. These bounds can be made arbitrarily close to the exact minimum probability of error, making them tighter than any previously known bounds.

引用

页码：89 / 91

页数：3

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