Least squares regression with \(l_1\)-regularizer in sum space

DOI10.1016/J.CAM.2013.11.029zbMath1278.62108OpenAlexW2035183799MaRDI QIDQ390496

Min Han, Xue-mei Dong, Min Wang, Yong-Li Xu

Publication date: 8 January 2014

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

Full work available at URL: https://doi.org/10.1016/j.cam.2013.11.029

zbMATH Keywords

learning theoryerror analysissum spaceleast square regressionregularization scheme

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Learning by nonsymmetric kernels with data dependent spaces and \(\ell^1\)-regularizer
• Concentration estimates for learning with \(\ell ^{1}\)-regularizer and data dependent hypothesis spaces
• Least square regression with indefinite kernels and coefficient regularization
• Model selection for regularized least-squares algorithm in learning theory
• Best choices for regularization parameters in learning theory: on the bias-variance problem.
• Regularization networks and support vector machines
• Learning rates of least-square regularized regression
• Learning theory estimates via integral operators and their approximations
• On the mathematical foundations of learning
• Least Square Regression with l^p-Coefficient Regularization
• Partially-Linear Least-Squares Regularized Regression for System Identification

This page was built for publication: Least squares regression with \(l_1\)-regularizer in sum space