Tight Margin-Based Generalization Bounds for Voting Classifiers over Finite Hypothesis Sets
Kasper Green Larsen ⋅ Natascha Schalburg
Abstract
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.
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