Learning with the Neyman-Pearson and Min-Max Criteria

摘要

We study two design criteria for classication: the Neyman-Pearson criterion and a min-max criterion. For each we prove a lemma bounding estimation error in terms of error deviance. We then show how these lemmas can be used to determine probabilistic guarantees on estimation error.