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34

ICANN
2005
Springer

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Smooth Bayesian Kernel Machines

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Smooth Bayesian Kernel Machines

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Abstract. In this paper, we consider the possibility of obtaining a kernel machine that is sparse in feature space and smooth in output space. Smooth in output space implies that the underlying function is supposed to have continuous derivatives up to some order. Smoothness is achieved by applying a roughness penalty, a concept from the area of functional data analysis. Sparseness is taken care of by automatic relevance determination. Both are combined in a Bayesian model, which has been implemented and tested. Test results are presented in the paper.

Rutger W. ter Borg, Léon J. M. Rothkrantz

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Added	27 Jun 2010
Updated	27 Jun 2010
Type	Conference
Year	2005
Where	ICANN
Authors	Rutger W. ter Borg, Léon J. M. Rothkrantz

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