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

Research output: Contribution to conferencePaper

179 Citations (Scopus)


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
Number of pages8
Publication statusPublished - Oct 2006
Event2006 International Joint Conference on Neural Networks - Vancouver, Canada
Duration: 16 Jul 200721 Jul 2007


Conference2006 International Joint Conference on Neural Networks
Abbreviated titleIJCNN-2006

Cite this