Learning rates for the kernel regularized regression with a differentiable strongly convex loss

DOI10.3934/cpaa.2020176zbMath1445.68193OpenAlexW3027856219MaRDI QIDQ2191832

Huanxiang Liu, Hui-min Wang, Bao Huai Sheng

Publication date: 26 June 2020

Published in: Communications on Pure and Applied Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/cpaa.2020176

zbMATH Keywords

\(K\)-functionallearning ratereproducing-kernel Hilbert spaceHutchinson metricconjugate lossdifferentiable strongly convex losskernel-regularized regressionmaximum mean discrepancy (MMD)

Mathematics Subject Classification ID

General nonlinear regression (62J02) Convex programming (90C25) 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 (4)

Learning rates of kernel-based robust classification ⋮ Convergence analysis for kernel-regularized online regression associated with an RRKHS ⋮ Error analysis of kernel regularized pairwise learning with a strongly convex loss ⋮ Error analysis of the kernel regularized regression based on refined convex losses and RKBSs

Uses Software

• ElemStatLearn
• ckn_kernel

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