English(EN) Benign Overfitting in Linear Classifiers with a Bias Term

新研究探讨带偏置项的线性分类器中的良性过拟合

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

研究人员 Yuta Kondo 和 Hashimoto 等人扩展了对线性回归模型中良性过拟合的分析。他们的工作现在包括带偏置项的分类器，这是先前类似研究中排除的一个特征。发现添加截距项会扰乱噪声的格拉姆矩阵，引入影响泛化能力的新约束。这些约束的影响因噪声的协方差而异，其中标签噪声的影响最强。 AI

影响这项研究改进了对线性模型如何泛化的理论理解，这可能为开发更鲁棒的机器学习算法提供信息。

排序理由关于机器学习主题的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Yuta Kondo · 2026-08-04 04:00

带偏置项的线性分类器中的良性过拟合

arXiv:2511.12840v2 Announce Type: replace Abstract: Overparameterized models often generalize well even when they interpolate noisy training data. This is known as benign overfitting. For linear classification, Hashimoto et al. (2025) analyzed the phenomenon under a broad class o…