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Bayesian wavelet networks for nonparametric regression.

Abstract:

Radial wavelet networks have recently been proposed as a method for nonparametric regression. In this paper we analyze their performance within a Bayesian framework. We derive probability distributions over both the dimension of the networks and the network coefficients by placing a prior on the degrees of freedom of the model. This process bypasses the need to test or select a finite number of networks during the modeling process. Predictions are formed by mixing over many models of varying ...

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Publication status:
Published

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Publisher copy:
10.1109/72.822507

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Role:
Author
Publisher:
IEEE
Journal:
IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council
Volume:
11
Issue:
1
Pages:
27-35
Publication date:
2000-01-01
DOI:
EISSN:
1941-0093
ISSN:
1045-9227
Source identifiers:
104743
Language:
English
Keywords:
Pubs id:
pubs:104743
UUID:
uuid:393c7bfc-5e5f-4b1d-a933-6cdb13fd4ce3
Local pid:
pubs:104743
Deposit date:
2012-12-19

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