Coefficient regularized regression with non-iid sampling

DOI10.1080/00207160.2011.587511zbMath1237.68165OpenAlexW2097285182MaRDI QIDQ2885538

Hongwei Sun, Qin Guo

Publication date: 23 May 2012

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

Full work available at URL: https://doi.org/10.1080/00207160.2011.587511

zbMATH Keywords

learning theoryapproximation errorstrong mixing conditionsample errorcoefficient regularized regression

Mathematics Subject Classification ID

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

Related Items (8)

Error analysis of the moving least-squares method with non-identical sampling ⋮ Convergence rate for the moving least-squares learning with dependent sampling ⋮ Distributed semi-supervised regression learning with coefficient regularization ⋮ Regression learning with non-identically and non-independently sampling ⋮ Coefficient-based regression with non-identical unbounded sampling ⋮ Coefficient-based \(l^q\)-regularized regression with indefinite kernels and unbounded sampling ⋮ Half supervised coefficient regularization for regression learning with unbounded sampling ⋮ Error analysis of the moving least-squares regression learning algorithm with β-mixing and non-identical sampling

Cites Work

• Unnamed Item
• Least square regression with indefinite kernels and coefficient regularization
• Regularized least square regression with dependent samples
• On regularization algorithms in learning theory
• A note on application of integral operator in learning theory
• Almost sure invariance principles for weakly dependent vector-valued random variables
• A distribution-free theory of nonparametric regression
• Least-square regularized regression with non-iid sampling
• Learning with sample dependent hypothesis spaces
• Application of integral operator for regularized least-square regression
• Learning theory estimates via integral operators and their approximations
• On the mathematical foundations of learning
• Spectral Algorithms for Supervised Learning
• ONLINE LEARNING WITH MARKOV SAMPLING
• 10.1162/153244302760200704
• Leave-One-Out Bounds for Kernel Methods
• The Invariance Principle for Stationary Processes

This page was built for publication: Coefficient regularized regression with non-iid sampling