新的PAC学习界限改进了确定性标签的样本复杂度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员为确定性无差别PAC学习开发了一个新的理论框架，改进了样本复杂度界限。该研究建立了样本复杂度与概念类的直径和VC维度之间的紧密关系。这项工作引入了新颖的基于记忆的算法，利用确定性标签结构来实现这些改进的界限，也对在线学习场景产生了影响。 AI

影响为机器学习样本复杂度建立了更严格的理论界限，可能影响未来的算法设计。

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arXiv cs.LG TIER_1 English(EN) · Shai Ben-David, Steve Hanneke, Farnam Mansouri, Amirreza Shaeiri · 2026-11-11 05:00

确定性无差别PAC学习的近乎最优样本复杂度

arXiv:2611.13210v1 Announce Type: cross Abstract: We study the problem of agnostic PAC learning under deterministic labels, where the examples are labeled by an arbitrary deterministic function, which need not belong to the concept class $C$. This model captures misspecification …