Improvement of multiple kernel learning using adaptively weighted regularization

DOI10.14495/JSIAML.5.49zbMath1414.62138OpenAlexW2024635867MaRDI QIDQ3121201

Taiji Suzuki

Publication date: 15 March 2019

Published in: JSIAM Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.14495/jsiaml.5.49

zbMATH Keywords

convergence ratekernel methodmachine learningmultiple kernel learning

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) 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) Linear operators in reproducing-kernel Hilbert spaces (including de Branges, de Branges-Rovnyak, and other structured spaces) (47B32)

Related Items (1)

Multiple Kernel Learningの学習理論

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• Weak convergence and empirical processes. With applications to statistics
• Local Rademacher complexities
• Support Vector Machines
• Model Selection and Estimation in Regression with Grouped Variables
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