Relevance Vector regression is a form of Support Vector regression, recently proposed by M.E.Tipping, which allows a sparse representation of the data. The Bayesian learning algorithm proposed by the author leaves the partially open question of how to automatically choose the optima! model. In this paper we describe a model selection criterion inspired by the Minimum Description Length (MDL) principie. We show that our proposal is effective in finding the optimal kernel parameter both on an artificial dataset and a real-world application.
MDL Based Model Selection for Relevance Vector Regression
ANGUITA, DAVIDE;
2002-01-01
Abstract
Relevance Vector regression is a form of Support Vector regression, recently proposed by M.E.Tipping, which allows a sparse representation of the data. The Bayesian learning algorithm proposed by the author leaves the partially open question of how to automatically choose the optima! model. In this paper we describe a model selection criterion inspired by the Minimum Description Length (MDL) principie. We show that our proposal is effective in finding the optimal kernel parameter both on an artificial dataset and a real-world application.
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