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
收藏
页码：89 / 91
页数：3
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