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arXiv · 2502.16462

Improved Margin Generalization Bounds for Voting Classifiers

Mikael Møller Høgsgaard·Kasper Green Larsen
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Abstract

In this paper we establish a new margin-based generalization bound for voting classifiers, refining existing results and yielding tighter generalization guarantees for widely used boosting algorithms such as AdaBoost (Freund and Schapire, 1997). Furthermore, the new margin-based generalization bound enables the derivation of an optimal weak-to-strong learner: a Majority-of-3 large-margin classifiers with an expected error matching the theoretical lower bound. This result provides a more natural alternative to the Majority-of-5 algorithm by (H{\o}gsgaard et al., 2024), and matches the Majority-of-3 result by (Aden-Ali et al., 2024) for the realizable prediction model.

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BibTeXRIS

Mikael Møller Høgsgaard, Kasper Green Larsen. 2025-02-23. Improved Margin Generalization Bounds for Voting Classifiers. https://arxiv.org/abs/2502.16462

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