Bayesian Support Vector Regression With Automatic Relevance Determination Kernel for Modeling of Antenna Input Characteristics

Automatic relevance determination for Least Squares Support Vector Machines classifiers

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.

Kernel Support Vector Regression with imprecise output ∗

We consider a regression problem where uncertainty affects to the dependent variable of the elements of the database. A model based on the standard -Support Vector Regression approach is given, where two hyperplanes need to be constructed to predict the interval-valued dependent variable. By using the Hausdorff distance to measure the error between predicted and real intervals, a convex quadrat...

Multiple Kernel Learning for Support Vector Regression ∗

Kernel support vector (SV) regression has successfully been used for prediction of nonlinear and complicated data. However, like other kernel methods such as support vector machine (SVM) classification, the quality of SV regression depends on proper choice of kernel functions and their parameters. Kernel selection for model selection is conventionally performed through repeated cross validation...

Support Vector Regression with Automatic Accuracy Control

A new algorithm for Support Vector regression is proposed. For a priori chosen , it automatically adjusts a exible tube of minimal radius to the data such that at most a fraction of the data points lie outside. The algorithm is analysed theoretically and experimentally.