Separating distribution-free and mistake-bound learning models over the Boolean domain

Two of the most commonly used models in computational learning theory are the distribution-free model, in which examples are chosen from a fixed but arbitrary distribution, and the absolute mistake-bound model, in which examples are presented in order by an adversary. Over the Boolean domain