Half supervised coefficient regularization for regression learning with unbounded sampling

DOI10.1080/00207160.2012.749985zbMath1333.68228OpenAlexW2090761569MaRDI QIDQ2855757

Xiaorong Chu, Hongwei Sun

Publication date: 22 October 2013

Published in: International Journal of Computer Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207160.2012.749985

zbMATH Keywords

learning theoryleast squares regressionsemi-supervised learningcoefficient-based regularizationunbounded sampling

Mathematics Subject Classification ID

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

Related Items (4)

Error analysis of the moving least-squares method with non-identical sampling ⋮ Convergence rate for the moving least-squares learning with dependent sampling ⋮ Regularized least square regression with unbounded and dependent sampling ⋮ Convergence rate of SVM for kernel-based robust regression

Cites Work

• Unnamed Item
• Integral operator approach to learning theory with unbounded sampling
• Concentration estimates for learning with \(\ell ^{1}\)-regularizer and data dependent hypothesis spaces
• Optimal learning rates for least squares regularized regression with unbounded sampling
• Least square regression with indefinite kernels and coefficient regularization
• On regularization algorithms in learning theory
• Elastic-net regularization in learning theory
• ERM learning with unbounded sampling
• Regularization networks and support vector machines
• The weight-decay technique in learning from data: an optimization point of view
• Optimal rates for the regularized least-squares algorithm
• Learning with sample dependent hypothesis spaces
• Application of integral operator for regularized least-square regression
• Learning rates of least-square regularized regression
• Learning theory estimates via integral operators and their approximations
• On the mathematical foundations of learning
• Coefficient regularized regression with non-iid sampling
• Spectral Algorithms for Supervised Learning
• SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
• Leave-One-Out Bounds for Kernel Methods

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