Journal article icon

Journal article

Robust Full Bayesian Learning for Radial Basis Networks

Abstract:

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 perform the Bayesian computation. We find that the results obtained using this method are not only better than the ones reported previously, but also appear to be robust with respect to the prior spe...

Expand abstract

Actions


Access Document


Publisher copy:
10.1162/089976601750541831

Authors


Christophe Andrieu More by this author
Nando De Freitas More by this author
Arnaud Doucet More by this author
Journal:
Neural Computation
Volume:
13
Issue:
10
Pages:
2359-2407
Publication date:
2001
DOI:
ISSN:
0899-7667
URN:
uuid:2d1248ee-9f32-4c33-b8be-9d2b37057a14
Local pid:
cs:7536

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP