Additive regularization trade-off: fusion of training and validation levels in kernel methods

DOI10.1007/s10994-005-5315-xzbMath1470.68157OpenAlexW2062112423MaRDI QIDQ2491372

Bart De Moor, Johan A. K. Suykens, Kristiaan Pelckmans

Publication date: 29 May 2006

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-005-5315-x

zbMATH Keywords

model selectionleast-squares support vector machinesoptimizatioonregulatization

Mathematics Subject Classification ID

Convex programming (90C25) Applications of mathematical programming (90C90) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

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Uses Software

• SenSrivastava

Cites Work

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• Bagging predictors
• Modeling by shortest data description
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers.
• Weighted least squares support vector machines: robustness and sparse approximation
• Best choices for regularization parameters in learning theory: on the bias-variance problem.
• Statistical predictor identification
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
• 10.1162/153244302760200704
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Some Comments on C P
• The elements of statistical learning. Data mining, inference, and prediction
• Choosing multiple parameters for support vector machines