论文分析数据增强对回归的正则化效应

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-11 09:52

研究人员分析了数据增强对监督回归方法的正则化效应，特别是在协变量数量与样本数量成比例的情况下。该研究基于真实数据的总体数量和增强过程的统计数据，精确地描述了测试误差（以均方误差为基础）。这些发现适用于具有错误指定特征映射和仅训练最后一层的模型架构，而网络的其余部分是固定的或随机初始化的。 AI

影响为数据增强对回归模型的影响提供了理论见解，可能为未来的模型训练策略提供信息。

排序理由该集群包含一篇详细阐述机器学习技术理论分析的学术论文。

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arXiv cs.LG TIER_1 English(EN) · Adrien Hardy · 2026-05-11 09:52

随机特征回归在任意数据增强下的泛化误差表征

This paper aims at analyzing the regularization effect that data augmentation induces on supervised regression methods in the proportional regime, where the number of covariates grows proportionally to the number of samples. We provide a tight characterization of the test error, …
Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-11 09:52

Arbitrary数据增强下随机特征回归的泛化误差表征

This paper aims at analyzing the regularization effect that data augmentation induces on supervised regression methods in the proportional regime, where the number of covariates grows proportionally to the number of samples. We provide a tight characterization of the test error, …