Optimal Bayesian Classification

Takes a Bayesian approach to modeling the feature-label distribution, and designs an optimal classifier relative to a posterior distribution governing an uncertainty class of feature-label distributions. The origins of this approach lie in estimating classifier error when there are insufficient data to hold out test data.