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Bayesian radial basis functions of variable dimension

Abstract:
A Bayesian framework for the analysis of radial basis functions (RBF) is proposed that accommodates uncertainty in the dimension of the model. A distribution is denned over the space of all RBF models of a given basis function, and posterior densities are computed using reversible jump Markov chain Monte Carlo samplers (Green, 1995). This alleviates the need to select the architecture during the modeling process. The resulting networks are shown to adjust their size to the complexity of the data.
Publication status:
Published

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Publisher copy:
10.1162/089976698300017421

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Role:
Author
Journal:
NEURAL COMPUTATION
Volume:
10
Issue:
5
Pages:
1217-1233
Publication date:
1998-07-01
DOI:
EISSN:
1530-888X
ISSN:
0899-7667
Source identifiers:
196286
Language:
English
Pubs id:
pubs:196286
UUID:
uuid:26118723-2d51-453f-acee-fce8b2220caa
Local pid:
pubs:196286
Deposit date:
2012-12-19

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