Error bounds for learning the kernel
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Publication:2835989
DOI10.1142/S0219530516400054zbMath1392.68357OpenAlexW102835753MaRDI QIDQ2835989
Ding-Xuan Zhou, Massimiliano Pontil, Qiang Wu, Charles A. Micchelli
Publication date: 30 November 2016
Published in: Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219530516400054
Related Items (14)
Feature space perspectives for learning the kernel ⋮ Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions ⋮ Learning with sample dependent hypothesis spaces ⋮ Fast learning rate of non-sparse multiple kernel learning and optimal regularization strategies ⋮ Conditional mean embedding and optimal feature selection via positive definite kernels ⋮ Optimality of regularized least squares ranking with imperfect kernels ⋮ Optimal regression rates for SVMs using Gaussian kernels ⋮ Error bounds for \(l^p\)-norm multiple kernel learning with least square loss ⋮ Bias corrected regularization kernel method in ranking ⋮ Reproducing kernels and choices of associated feature spaces, in the form of \(L^2\)-spaces ⋮ Learning and approximation by Gaussians on Riemannian manifolds ⋮ Generalized support vector regression: Duality and tensor-kernel representation ⋮ Error bounds for learning the kernel ⋮ Learning rates for partially linear support vector machine in high dimensions
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