Abstract: In this paper, we show that training of the support vector machine (SVM) can be interpreted as performing the level 1 inference of MacKay's evidence framework. We further on show that levels 2 and 3 can also be applied to SVM. This allows automatic adjustment of the regularization parameter and the kernel parameter. More importantly, it opens up a wealth of Bayesian tools for use with SVM. Performance is evaluated on both synthetic and real-world data sets.
Proceedings of the European Symposium on Artificial Neural Networks (ESANN), pp.177-182, Bruges, Belgium, April 1999.