提出更紧的投票分类器泛化界，改进提升提升AdaBoost等算法性能。
Improved Margin Generalization Bounds for Voting Classifiers
- 基于新边际泛化界设计更强学习器，优化投票机制。
- 实现3个大边距分类器的多数表决，误差逼近理论下界。
- 为真实可分模型提供更自然的最优弱到强学习方案。
本文建立了投票分类器的新边际泛化界，改进了现有结果，对AdaBoost等广泛应用的提升算法提供了更紧的泛化保证。该边界进一步导出一个最优弱到强学习器：由3个大边距分类器组成的多数表决结构，其期望误差达到理论下界。这一结果为（Høgsgaard et al., 2024）提出的五数多数算法提供了更自然的替代方案，并与（Aden-Ali et al., 2024）在可实现预测模型下的三数多数结果一致。
原文摘要 · Abstract (English)
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 (Høgsgaard et al., 2024), and matches the Majority-of-3 result by (Aden-Ali et al., 2024) for the realizable prediction model.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。