Sparse Kernel Modelling: A Unified Approach
Sparse Kernel Modelling: A Unified Approach
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.
27-36
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
December 2007
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, S., Hong, X. and Harris, C.J.
(2007)
Sparse Kernel Modelling: A Unified Approach.
8th International Conference on Intelligent Data Engineering and Automated Learning, Birmingham, United Kingdom.
16 - 19 Dec 2007.
.
Record type:
Conference or Workshop Item
(Other)
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.
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More information
Published date: December 2007
Additional Information:
Event Dates: December 16-19, 2007
Venue - Dates:
8th International Conference on Intelligent Data Engineering and Automated Learning, Birmingham, United Kingdom, 2007-12-16 - 2007-12-19
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 264986
URI: http://eprints.soton.ac.uk/id/eprint/264986
PURE UUID: fcb454ae-ea06-4bb5-8ca9-4d88faf38234
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Date deposited: 20 Dec 2007 11:16
Last modified: 14 Mar 2024 08:00
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Contributors
Author:
S. Chen
Author:
X. Hong
Author:
C.J. Harris
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