Relevance vector machine: Difference between revisions

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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. However RVMs use a gradient-ascent learning method and are therefore at risk of local minima, unlike the standard [SMO] based algorithms employed by [SVM]s which are guaranteed to find a global optimum.

External links

The Relevance Vector Machine Tipping's article on the relevance vector machine.
Tipping's webpage on Sparse Bayesian Models and the RVM

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 '''Relevance Vector Machine (RVMs)''' is a [[machine learning]] technique that uses [[Bayesian theory]] to obtain sparse solutions for [[regression]] and [[Statistical classification|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.
+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. However RVMs use a gradient-ascent learning method and are therefore at risk of local minima, unlike the standard [SMO] based algorithms employed by [SVM]s which are guaranteed to find a global optimum.
 ==External links==