Fast learning rates for regularized regression algorithms

DOI10.1360/012012-74zbMath1488.68051OpenAlexW3149544667MaRDI QIDQ5017723

Fenghong Yang, Hongzhi Tong, Di-Rong Chen

Publication date: 17 December 2021

Published in: SCIENTIA SINICA Mathematica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1360/012012-74

zbMATH Keywords

regularizationreproducing kernel Hilbert spacesempirical processeslearning ratesregression algorithm

Mathematics Subject Classification ID

General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22)

Related Items (5)

Learning performance of regularized moving least square regression ⋮ Learning rates for the kernel regularized regression with a differentiable strongly convex loss ⋮ Moving quantile regression ⋮ Analysis of Regression Algorithms with Unbounded Sampling ⋮ Error analysis of the kernel regularized regression based on refined convex losses and RKBSs

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