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.

翻译：我们研究了可实现设定下的在线非参数分类实例。具体而言，我们考虑了经典的1-最近邻算法，并证明该算法在可实现设定下，面对支配性对手或平滑对手时，能够实现次线性遗憾——即错误率趋近于零。