Error analysis on regularized regression based on the Maximum correntropy criterion

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 01 Jan 2020 English Publisher:American Institute of Mathematical Sciences (AIMS)Journal:Mathematical Foundations of Computing, volume 3, pages 25-40 (issn: 2577-8838,

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Authors: Bingzheng Li; Zhengzhan Dai;

doi: 10.3934/mfc.2020003

Error analysis on regularized regression based on the Maximum correntropy criterion

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Abstract

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Keywords

covering number, reproducing kernel Hilbert spaces, learning theory, maximum correntropy, Learning and adaptive systems in artificial intelligence, General nonlinear regression, regularization error, Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces)

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