A NOTE ON STABILITY OF ERROR BOUNDS IN STATISTICAL LEARNING THEORY

DOI10.1142/S0219530511001893zbMath1267.68185OpenAlexW2046237749WikidataQ60700494 ScholiaQ60700494MaRDI QIDQ3096969

Ming Li, Andrea Caponnetto

Publication date: 15 November 2011

Published in: Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219530511001893

zbMATH Keywords

learning theoryerror boundsregressionstability of learning algorithms

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30)

Cites Work

• Model selection for regularized least-squares algorithm in learning theory
• On regularization algorithms in learning theory
• Regularization networks and support vector machines
• Optimal rates for the regularized least-squares algorithm
• Learning theory estimates via integral operators and their approximations
• On the mathematical foundations of learning
• Consistency of Support Vector Machines and Other Regularized Kernel Classifiers
• CROSS-VALIDATION BASED ADAPTATION FOR REGULARIZATION OPERATORS IN LEARNING THEORY

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