Robust full Bayesian learning for radial basis networks.

Robust full Bayesian learning for radial basis networks.

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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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Dettagli Bibliografici
Autori principali:	Andrieu, C, de Freitas, N, Doucet, A
Natura:	Journal article
Lingua:	English
Pubblicazione:	2001

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