Another look at statistical learning theory and regularization

DOI10.1016/j.neunet.2009.04.005zbMath1335.68197DBLPjournals/nn/CherkasskyM09OpenAlexW2032314390WikidataQ43609262 ScholiaQ43609262MaRDI QIDQ280403

Yunqian Ma, Vladimir Cherkassky

Publication date: 10 May 2016

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2009.04.005

zbMATH Keywords

regularizationpenalizationridge regressionfunction approximationmodel identificationpredictive learningstatistical model estimationstructural risk minimizationSVM regressionVC-theory

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Generalization ability of fractional polynomial models

Uses Software

• ElemStatLearn

Cites Work

• Interpolation of scattered data: distance matrices and conditionally positive definite functions
• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training
• Risk bounds for model selection via penalization
• Practical selection of SVM parameters and noise estimation for SVM regression
• Regularization networks and support vector machines
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• Universal approximation bounds for superpositions of a sigmoidal function
• Ideal spatial adaptation by wavelet shrinkage
• Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• An introduction to the mathematical theory of inverse problems
• The elements of statistical learning. Data mining, inference, and prediction
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