Robust full Bayesian learning for radial basis networks.

We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model dimension (number of neurons), model parameters, regularization parameters, and noise parameters as unknown random variables. We develop a reversible-jump Markov chain Monte Carlo (MCMC) method to pe...

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Détails bibliographiques
Auteurs principaux:	Andrieu, C, de Freitas, N, Doucet, A
Format:	Journal article
Langue:	English
Publié:	2001

Robust Full Bayesian Learning for Radial Basis Networks par Andrieu, C, De Freitas, N, Doucet, A

Publié 2001

Journal article

Robust full Bayesian learning for radial basis networks.

Robust Full Bayesian Learning for Radial Basis Networks par Andrieu, C, De Freitas, N, Doucet, A

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