English(EN) A Data-dependent Early Stopping Rule using Rademacher Complexity with L1-norm

新的提前停止规则使用 Rademacher 复杂度来处理神经网络

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-26 04:00

研究人员开发了一种新的数据依赖型提前停止规则，用于训练神经网络，利用了带有 L1 范数的 Rademacher 复杂度。该分析框架旨在估计最佳停止点，而无需像现有的数值方法那样进行梯度下降训练。该方法侧重于线性模型，但可以通过线性探测应用于非线性神经网络，如在 MNIST 数据库上的分类示例所示。 AI

影响这种新的提前停止规则通过提供一种分析方法来确定最佳停止点，有望实现更高效、更鲁棒的神经网络训练。

排序理由该集群包含一篇详细介绍神经网络新训练方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Duy Hoang, Bastien Berret, Olivier Bruneau, Laurent Fribourg · 2026-08-26 04:00

一种基于Rademacher复杂度与L1范数的依赖数据的早期停止规则

arXiv:2608.24210v1 Announce Type: new Abstract: Training neural networks requires balancing the trade-off between fitting the training data and achieving robust performance on unseen inputs. This ability, commonly referred to as generalizability, is determined by the gap between …

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报道来源 [1]

一种基于Rademacher复杂度与L1范数的依赖数据的早期停止规则

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