Title: A convex approach to validation-based learning of the regularization constant
Authors: Pelckmans, Kristiaan ×
Suykens, Johan
De Moor, Bart #
Issue Date: May-2007
Publisher: Institute of Electrical and Electronics Engineers
Series Title: IEEE Transactions on Neural Networks vol:18 issue:3 pages:917-920
Abstract: This letter investigates a tight convex relaxation to the problem of tuning the regularization constant with respect to a validation based criterion. A number of algorithms is covered including ridge regression, regularization networks, smoothing splines, and least squares support vector machines (LS-SVMs) for regression. This convex approach allows the application of reliable and efficient tools, thereby improving computational cost and automatization of the learning method. It is shown that all solutions of the relaxation allow an interpretation in terms of a solution to a weighted LS-SVM.
ISSN: 1045-9227
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Electrical Engineering - miscellaneous
× corresponding author
# (joint) last author

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