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BOLT: Universal Classifier Training via a Bayes-Error Upper Bound
computer-vision-and-pattern-recognitioni-2-6i-5-4image-and-video-processinginformation-theorymachine-learningsignal-processing
This work introduces a novel approach to compute a universal upper bound on the Bayes error rate (BER) from classifier-output samples, which serves as a Bayes-error-related performance criterion for the underlying classification task. We show that the bound is tight in the binary case and characteri
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