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.

翻译：本文为投票分类器建立了一种新的基于间隔的泛化界，改进了现有结果，并为广泛使用的提升算法（如AdaBoost）提供了更紧的泛化保证。此外，新的基于间隔的泛化界使得推导一种最优的弱到强学习器成为可能：一个由三个大间隔分类器构成的多数投票集成，其期望误差与理论下界相匹配。该结果为（Høgsgaard等人，2024）提出的“多数-5”算法提供了一种更自然的替代方案，并且在可实现预测模型下，与（Aden-Ali等人，2024）的“多数-3”结果相一致。