DocumentCode
1082479
Title
Arbitrarily tight upper and lower bounds on the Bayesian probability of error
Author
Avi-Itzhak, H. ; Diep, Thanh
Author_Institution
Canon Res. Center America, Palo Alto, CA, USA
Volume
18
Issue
1
fYear
1996
fDate
1/1/1996 12:00:00 AM
Firstpage
89
Lastpage
91
Abstract
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
Keywords
Bayes methods; decision theory; error statistics; optimisation; pattern recognition; probability; statistical analysis; Bayesian decision systems; Bayesian probability; error probability; lower bounds; minimum probability; statistical pattern recognition; two-class problem; upper bound; Bayesian methods; Closed-form solution; Density functional theory; Entropy; Information analysis; Information systems; Laboratories; Machine intelligence; Pattern recognition; Probability density function;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.476017
Filename
476017
Link To Document