Error bounds for learning the kernel

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

zbMATH Keywords

regularizationkernelsupport vector machineleast squareGaussianserror decomposition

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

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

Cites Work

• Consistency analysis of an empirical minimum error entropy algorithm
• Logistic classification with varying gaussians
• Multi-kernel regularized classifiers
• Weak convergence and empirical processes. With applications to statistics
• Learning with sample dependent hypothesis spaces
• Error bounds for learning the kernel
• Learning Theory
• ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
• 10.1162/153244303321897690
• Regularization schemes for minimum error entropy principle
• Algorithmic Learning Theory
• Learning Theory
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Error bounds for learning the kernel