REGULARIZED LEAST SQUARE ALGORITHM WITH TWO KERNELS

DOI10.1142/S0219691312500439zbMath1278.68112OpenAlexW2054448544MaRDI QIDQ4917257

Ping Liu, Hongwei Sun

Publication date: 29 April 2013

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/s0219691312500439

zbMATH Keywords

learning theorycovering numberleast squares regressionkernel-based regularizationmulti-kernel algorithms

Mathematics Subject Classification ID

Computational learning theory (68Q32) General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Learning rates of kernel-based robust classification ⋮ Application of integral operator for vector-valued regression learning

Cites Work

• On regularization algorithms in learning theory
• Best choices for regularization parameters in learning theory: on the bias-variance problem.
• Regularization networks and support vector machines
• Application of integral operator for regularized least-square regression
• Learning rates of least-square regularized regression
• Shannon sampling. II: Connections to learning theory
• Learning theory estimates via integral operators and their approximations
• On the mathematical foundations of learning
• GENERALIZATION BOUNDS OF REGULARIZATION ALGORITHMS DERIVED SIMULTANEOUSLY THROUGH HYPOTHESIS SPACE COMPLEXITY, ALGORITHMIC STABILITY AND DATA QUALITY
• Choosing multiple parameters for support vector machines

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