Analysis of support vector machines regression

DOI10.1007/s10208-008-9026-0zbMath1185.68577OpenAlexW1982453328MaRDI QIDQ1022433

Di-Rong Chen, Hongzhi Tong, Li Zhong Peng

Publication date: 22 June 2009

Published in: Foundations of Computational Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10208-008-9026-0

Mathematics Subject Classification ID

General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (20)

The kernel regularized learning algorithm for solving Laplace equation with Dirichlet boundary ⋮ Learning rates for the kernel regularized regression with a differentiable strongly convex loss ⋮ Approximation analysis of learning algorithms for support vector regression and quantile regression ⋮ Online learning for quantile regression and support vector regression ⋮ Learning with Convex Loss and Indefinite Kernels ⋮ Support vector machines regression with unbounded sampling ⋮ A simpler approach to coefficient regularized support vector machines regression ⋮ Learning with varying insensitive loss ⋮ Learning rate of support vector machine for ranking ⋮ Calibration of \(\epsilon\)-insensitive loss in support vector machines regression ⋮ Posterior consistency of semi-supervised regression on graphs ⋮ Learning rates for regularized classifiers using multivariate polynomial kernels ⋮ The convergence rate for a \(K\)-functional in learning theory ⋮ Support vector machines regression with \(l^1\)-regularizer ⋮ Learning rates of multi-kernel regularized regression ⋮ Analysis of Regression Algorithms with Unbounded Sampling ⋮ Regularized ranking with convex losses and \(\ell^1\)-penalty ⋮ Optimal rate for support vector machine regression with Markov chain samples ⋮ Generalization performance of Gaussian kernels SVMC based on Markov sampling ⋮ Error analysis of the kernel regularized regression based on refined convex losses and RKBSs

Cites Work

• Model selection for regularized least-squares algorithm in learning theory
• Best choices for regularization parameters in learning theory: on the bias-variance problem.
• The covering number in learning theory
• Regularization networks and support vector machines
• Consistency and robustness of kernel-based regression in convex risk minimization
• Approximation with polynomial kernels and SVM classifiers
• Learning rates of least-square regularized regression
• Shannon sampling. II: Connections to learning theory
• Learning theory estimates via integral operators and their approximations
• On the mathematical foundations of learning
• Capacity of reproducing kernel spaces in learning theory
• The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
• SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
• 10.1162/153244302760200704
• Are Loss Functions All the Same?
• Learning Theory
• Theory of Reproducing Kernels
• Robust Statistics
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Analysis of support vector machines regression