Sparse kernel modelling: a unified approach

Authors:
S. Chen;X. Hong;C. J. Harris
Affiliations:
School of Electronics and Computer Sciences, University of Southampton, Southampton, U.K.;School of Systems Engineering, University of Reading, Reading, U.K.;School of Electronics and Computer Sciences, University of Southampton, Southampton, U.K.
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

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Abstract

A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach.