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 leve ls 2 and 3 of the evidence framework can also be applied to SVMs. This integration allows automatic adjustment of the regularization parameter and the kernel parameter to t heir near-optimal values. Moreover, it opens up a wealth of Bayesian tools for use with SVMs. Performance of this method is evaluated on both synthetic and real-world data sets.
IEEE Transactions on Neural Networks, 11(5):1162-1173, September 2000.
Postscript: http://www.cs.ust.hk/~jamesk/papers/tnn00.ps.gz