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Sparse kernel modelling: a unified approach

Chen, S., Hong, X. and Harris, C.J. (2007) Sparse kernel modelling: a unified approach. In: 8th International Conference on Intelligent Data Engineering and Automated Learning, Birmingham, UK.

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Abstract/Summary

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.

Item Type:Conference or Workshop Item (Paper)
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:14627
Uncontrolled Keywords:ORTHOGONAL LEAST-SQUARES, LOCAL REGULARIZATION, REGRESSION, ALGORITHM
Publisher:Springer-Verlag

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