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On the structure of strict sense Bayesian cost functions and its applications

In the context of classification problems, the paper analyzes the general structure of the strict sense Bayesian (SSB) cost functions, those having a unique minimum when the soft decisions are equal to the posterior class probabilities. We show that any SSB cost is essentially the sum of a generalized measure of entropy, which does not depend on the targets, and an error component. Symmetric cost functions are analyzed in detail. Our results provide a further insight on the behavior of this family of objective functions and are the starting point for the exploration of novel algorithms. Two applications are proposed. First, the use of asymmetric SSB cost functions for posterior probability estimation in non-maximum a posteriori (MAP) decision problems. Second, a novel entropy minimization principle for hybrid learning: use labeled data to minimize the cost function, and unlabeled data to minimize the corresponding entropy measure.