  We study an instance of online non-parametric classification in the
realizable setting. In particular, we consider the classical 1-nearest neighbor
algorithm, and show that it achieves sublinear regret - that is, a vanishing
mistake rate - against dominated or smoothed adversaries in the realizable
setting.
