Variational Bayesian Approach to Support Vector Regression

Bayesian Support Vector Regression

We show that the Bayesian evidence framework can be applied to both-support vector regression (-SVR) and-support vector regression (-SVR) algorithms. Standard SVR training can be regarded as performing level one inference of the evidence framework, while levels two and three allow automatic adjustments of the regularization and kernel parameters respectively, without the need of a validation set.

A geometric approach to support vector regression

We develop an intuitive geometric framework for support vector regression (SVR). By examining when 2-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard and soft 2-tubes are constructed by separating the convex or reduced convex hulls respectively of the training data with the response variable shifted up and down by 2. A novel SVR model is proposed...

Bayesian Inference in Support Vector Regression

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A variational approach to Bayesian logistic regression models andtheir

We consider a logistic regression model with a Gaussian prior distribution over the parameters. We show that accurate variational techniques can be used to obtain a closed form posterior distribution over the parameters given the data thereby yielding a posterior predictive model. The results are readily extended to (binary) belief networks. For belief networks we also derive closed form poster...

Applying the Bayesian Evidence Framework to \nu -Support Vector Regression