Title: Morozov, Ivanov and Tikhonov regularization based LS-SVMs
Authors: Pelckmans, Kristiaan ×
Suykens, Johan
De Moor, Bart #
Issue Date: 2004
Publisher: Springer-verlag berlin
Host Document: Proc. of the11th International Conference on Neural Information Processing (ICONIP 2004) vol:3316 pages:1216-1222
Conference: 11th International Conference on Neural Information Processing (ICONIP 2004) location:Calcutta, India date:Nov. 2004
Abstract: This paper contrasts three related regularization schemes for kernel machines using a least squares criterion, namely Tikhonov and Ivanov regularization and Morozov's discrepancy principle. We derive the conditions for optimality in a least squares support vector machine context (LS-SVMs) where they differ in the role of the regularization parameter. In particular, the Ivanov and Morozov scheme express the trade-off between data-fitting and smoothness in the trust region of the parameters and the noise level respectively which both can be transformed uniquely to an appropriate regularization constant for a standard LS-SVM. This insight is employed to tune automatically the regularization constant in an LS-SVM framework based on the estimated noise level, which can be obtained by using a nonparametric technique as e.g. the differogram estimator.
Description: \emph{Proc. of the11th International Conference on Neural Information Processing (ICONIP 2004)}, Calcutta, India, Nov. 2004
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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