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.
|Number of pages
|Published - Oct 2006
|2006 International Joint Conference on Neural Networks - Vancouver, Canada
Duration: 16 Jul 2007 → 21 Jul 2007
|2006 International Joint Conference on Neural Networks
|16/07/07 → 21/07/07