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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Detalhes bibliográficos
Main Authors:	Andrieu, C, de Freitas, N, Doucet, A
Formato:	Journal article
Idioma:	English
Publicado em:	2001

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

Publicado em 2001

Journal article

Robust full Bayesian learning for radial basis networks.

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

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