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

Research output: Contribution to conferencePaper

155 Citations (Scopus)

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.
Original languageEnglish
Pages1661-1668
Number of pages8
DOIs
Publication statusPublished - Oct 2006
EventProceedings of the International Joint Conference on Neural Networks (IJCNN-2006) - Vancouver, BC
Duration: 1 Oct 2006 → …

Conference

ConferenceProceedings of the International Joint Conference on Neural Networks (IJCNN-2006)
CityVancouver, BC
Period1/10/06 → …

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