Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs

Cawley, G. C. ORCID: https://orcid.org/0000-0002-4118-9095 (2006) Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs. In: 2006 International Joint Conference on Neural Networks, 2007-07-16 - 2007-07-21.

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Abstract

While the model parameters of many kernel learning methods are given by the solution of a convex optimisation problem, the selection of good values for the kernel and regularisation parameters, i.e. model selection, is much less straight-forward. This paper describes a simple and efficient approach to model selection for weighted least-squares support vector machines, and compares a variety of model selection criteria based on leave-one-out cross-validation. An external cross-validation procedure is used for performance estimation, with model selection performed independently in each fold to avoid selection bias. The best entry based on these methods was ranked in joint first place in the WCCI-2006 performance prediction challenge, demonstrating the effectiveness of this approach.

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
Related URLs:	http://theoval.cmp.uea.ac.uk/publication...
Depositing User:	Vishal Gautam
Date Deposited:	20 May 2011 12:14
Last Modified:	22 Apr 2023 02:45
URI:	https://ueaeprints.uea.ac.uk/id/eprint/23362
DOI:	10.1109/IJCNN.2006.246634

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