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

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2 Author(s)
H. Avi-Itzhak ; Canon Res. Center America, Palo Alto, CA, USA ; Thanh Diep

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

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:18 ,  Issue: 1 )