On the K-functional in learning theory
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Publication:5107666
DOI10.1142/S0219530519500192zbMath1434.68452OpenAlexW2977330234MaRDI QIDQ5107666
Publication date: 28 April 2020
Published in: Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219530519500192
learning theoryspherical harmonics\(K\)-functionalconvergence ratemodulus of smoothnessFourier seriesunit sphereunit ball
Learning and adaptive systems in artificial intelligence (68T05) Rate of convergence, degree of approximation (41A25) Approximation by operators (in particular, by integral operators) (41A35) Convergence and absolute convergence of Fourier and trigonometric series (42A20) Linear operators in reproducing-kernel Hilbert spaces (including de Branges, de Branges-Rovnyak, and other structured spaces) (47B32)
Related Items (5)
Learning rates of kernel-based robust classification ⋮ Learning rates for the kernel regularized regression with a differentiable strongly convex loss ⋮ Convergence of online pairwise regression learning with quadratic loss ⋮ Convergence analysis for kernel-regularized online regression associated with an RRKHS ⋮ Error analysis of the kernel regularized regression based on refined convex losses and RKBSs
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