SVM regression through variational methods and its sequential implementation
We consider an SVM regression model based on kernel methods with a Gaussian prior distribution over the network parameters. We show that the variational techniques can be utilised to obtain a closed form a posteriori distribution over the parameters given the data hence yielding an a posteriori predictive model.
regression support vector machine bayesian inference bayesian variational algorithm em algorithm