Tight Margin-Based Generalization Bounds for Voting Classifiers over Finite Hypothesis Sets

TL;DR

This paper introduces a new margin-based generalization bound for voting classifiers over finite hypothesis sets, which is asymptotically tight and accounts for multiple factors affecting learning performance.

Contribution

It provides the first asymptotically tight margin-based generalization bound specifically for voting classifiers over finite hypothesis sets.

Findings

Bound is asymptotically tight in key parameters

Accounts for hypothesis set size, margin, training points, samples, and failure probability

Advances theoretical understanding of voting classifier generalization

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.