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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書誌詳細
主要な著者:	Andrieu, C, de Freitas, N, Doucet, A
フォーマット:	Journal article
言語:	English
出版事項:	2001

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

出版事項 2001

Journal article

Robust full Bayesian learning for radial basis networks.

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

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