MDL Based Model Selection for Relevance Vector Regression

Authors:
Davide Anguita;Matteo Gagliolo
Affiliations:
-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 2
Cited 0

Stochastic Complexity in Statistical Inquiry Theory

Stochastic Complexity in Statistical Inquiry Theory
Sparse bayesian learning and the relevance vector machine

The Journal of Machine Learning Research

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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 optimal model.In this paper we describe a model selection criterion inspired by the Minimum Description Length (MDL) principle. We show that our proposal is effective in finding the optimal kernel parameter both on an artificial dataset and a real-world application.