Coefficient-based regularized regression with dependent and unbounded sampling

DOI10.1142/S0219691316500399zbMath1419.62086OpenAlexW2468357046MaRDI QIDQ2819179

Qin Guo, Pei Xin Ye

Publication date: 28 September 2016

Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219691316500399

zbMATH Keywords

error boundsreproducing kernel Hilbert spacelearning ratescoefficient-based regularization schememixing samples

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (6)

Error analysis of the moving least-squares method with non-identical sampling ⋮ Convergence rate for the moving least-squares learning with dependent sampling ⋮ Error analysis for \(l^q\)-coefficient regularized moving least-square regression ⋮ Coefficient-based \(l^q\)-regularized regression with indefinite kernels and unbounded sampling ⋮ Analysis of Regression Algorithms with Unbounded Sampling ⋮ Error analysis of the moving least-squares regression learning algorithm with β-mixing and non-identical sampling

Cites Work

• Regularized least square regression with unbounded and dependent sampling
• Learning rates for least square regressions with coefficient regularization
• On the mathematical foundations of learning
• Application of integral operator for vector-valued regression learning
• Learning by atomic norm regularization with polynomial kernels
• Optimal rate for support vector machine regression with Markov chain samples
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