Relevance Vector Machine

Relevance Vector Machine (RVMs) is a machine learning technique that uses Bayesian theory to obtain sparse solutions for regression and classification. The RVM has an identical functional form to the Support Vector Machine, but provides probabilistic classification.

Compared to the SVM the Bayesian formulation allows to avoid the set of free parameters that the SVM have and that usually require cross-validation based post optimizations.