BriefGPT.xyz
Oct, 2020
高维核回归：超越双谷现象的细致分析
Kernel regression in high dimension: Refined analysis beyond double descent
HTML
PDF
Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens
TL;DR
该研究通过建立偏差-方差分解方法，研究了高维核岭回归在欠参数和过参数情况下的泛化性能特征， 揭示了特定的正则化方案下偏差和方差与训练数据数量n 和特征维度d的组合方式对核回归风险曲线的形状的影响。
Abstract
In this paper, we provide a precise characterize of
generalization properties
of high dimensional
kernel ridge regression
across the under- and over-parameterized regimes, depending on whether the number of train
→