Tight Margin-Based Generalization Bounds for Voting Classifiers over Finite Hypothesis Sets
This work provides a foundational theoretical result for machine learning practitioners and researchers, offering precise guarantees for voting classifiers, though it is incremental in advancing existing margin theory.
The authors tackled the problem of deriving generalization bounds for voting classifiers by proving the first margin-based bound that is asymptotically tight across key parameters, including hypothesis set size, margin, training sample count, and failure probability.
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.