Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coecients of the kernel expansion being determined at the rst level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-tting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classier, at the
['Gavin C. Cawley', 'Nicola L. C. Talbot']
Kernel learning at the first level of inference