AIC and BIC based approaches for SVM parameter value estimation with RBF kernels

We study the problem of selecting the best parameter values to use for a support vector machine (SVM) with RBF kernel. Our methods extend the well-known formulas for AIC and BIC, and we present two alternative approaches for calculating the necessary likelihood functions for these formulas. Our first approach is based on using the distances of support vectors from the separating hyperplane. Our second approach estimates the probability that the SVM hyperplane coincides with the Bayes classifier, by analysing the disposition of points in the kernel feature space. We experimentally compare our two approaches with several existing methods and show they are able to achieve good accuracy, whilst also having low running time.