Learning Rates of lq Coefficient Regularization Learning with Gaussian Kernel

DOI10.1162/NECO_a_00641zbMath1410.62119arXiv1312.5465WikidataQ50646399 ScholiaQ50646399MaRDI QIDQ5175497

Jinshan Zeng, Jian Fang, Shao-Bo Lin, Zong Ben Xu

Publication date: 23 February 2015

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1312.5465

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (8)

Nonparametric regression using needlet kernels for spherical data ⋮ Unnamed Item ⋮ Convergence of online pairwise regression learning with quadratic loss ⋮ Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery ⋮ Learning Rates for Classification with Gaussian Kernels ⋮ Nyström subsampling method for coefficient-based regularized regression ⋮ Distributed learning with indefinite kernels ⋮ Optimal rates for coefficient-based regularized regression

Cites Work

• Concentration estimates for learning with \(\ell ^{1}\)-regularizer and data dependent hypothesis spaces
• Least square regression with indefinite kernels and coefficient regularization
• Unified approach to coefficient-based regularized regression
• Regularization in kernel learning
• Learning and approximation by Gaussians on Riemannian manifolds
• Fast rates for support vector machines using Gaussian kernels
• Reproducing kernel Banach spaces with the \(\ell^1\) norm
• A distribution-free theory of nonparametric regression
• The covering number in learning theory
• Optimal rates for the regularized least-squares algorithm
• Learning with sample dependent hypothesis spaces
• Statistical performance of support vector machines
• Approximation methods for supervised learning
• Approximation and learning by greedy algorithms
• Approximation with polynomial kernels and SVM classifiers
• Learning rates of least-square regularized regression
• On the mathematical foundations of learning
• Least Square Regression with l^p-Coefficient Regularization
• ONLINE REGRESSION WITH VARYING GAUSSIANS AND NON-IDENTICAL DISTRIBUTIONS
• Reproducing Kernel Banach Spaces with the ℓ¹ Norm II: Error Analysis for Regularized Least Square Regression
• Learning Theory
• An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels
• Iteratively reweighted least squares minimization for sparse recovery
• An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
• SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
• Some properties of Gaussian reproducing kernel Hilbert spaces and their implications for function approximation and learning theory

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