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Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs | IEEE Conference Publication | IEEE Xplore

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


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 reg...Show More

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.
Date of Conference: 16-21 July 2006
Date Added to IEEE Xplore: 30 October 2006
Print ISBN:0-7803-9490-9

ISSN Information:

Conference Location: Vancouver, BC, Canada

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