Moving least-square method in learning theory

DOI10.1016/j.jat.2009.12.002zbMath1202.68325OpenAlexW1969696041MaRDI QIDQ968963

Hong-Yan Wang, Dao-Hong Xiang, Ding-Xuan Zhou

Publication date: 11 May 2010

Published in: Journal of Approximation Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jat.2009.12.002

zbMATH Keywords

learning theoryapproximation errorsample errormoving least-square methodnorming condition

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (8)

Error analysis of the moving least-squares method with non-identical sampling ⋮ Learning performance of regularized moving least square regression ⋮ Convergence rate for the moving least-squares learning with dependent sampling ⋮ Error analysis for \(l^q\)-coefficient regularized moving least-square regression ⋮ Concentration estimates for the moving least-square method in learning theory ⋮ Statistical analysis of the moving least-squares method with unbounded sampling ⋮ Moving quantile regression ⋮ Error analysis of the moving least-squares regression learning algorithm with β-mixing and non-identical sampling

Uses Software

• KernSmooth

Cites Work

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• Regularization networks and support vector machines
• Learning theory estimates via integral operators and their approximations
• Local polynomial reproduction and moving least squares approximation
• Learning Theory
• The approximation power of moving least-squares
• Error estimates for scattered data interpolation on spheres
• Besov Spaces on Closed Subsets of ℝ n
• Shannon sampling and function reconstruction from point values
• Scattered Data Approximation

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