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ICONIP
2004

Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs

14 years 28 days ago
Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs
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.
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where ICONIP
Authors Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
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