We prove the first margin-based generalization bound for voting classifiers, that is asymptotically tight in the tradeoff between the size of the hypothesis set, the margin, the fraction of training points with the given margin, the number of training samples and the failure probability.

翻译：我们首次证明了投票分类器的边际泛化界，该界在假设集大小、边际值、具有给定边际的训练点比例、训练样本数量以及失败概率之间的权衡关系上是渐近紧致的。