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ABSTRACT
<p><b>Abstract</b>—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.</p>
INDEX TERMS
Bayesian decision, probability of error, statistical pattern recognition.
CITATION

H. Avi-Itzhak and T. Diep, "Arbitrarily Tight Upper and Lower Bounds on the Bayesian Probability of Error," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 18, no. , pp. 89-91, 1996.
doi:10.1109/34.476017
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