par Anguita, Davide;Gagliolo, Matteo
Référence Lecture notes in computer science, 2415, page (468-473)
Publication Publié, 2002-08
Article révisé par les pairs
Résumé : 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.