Arbitrarily Tight Upper and Lower Bounds on the Bayesian Probability of Error

Authors:
Hadar Avi-Itzhak;Thanh Diep
Affiliations:
-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1996

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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.