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Estimations of error bounds for neural-network function approximators.

Abstract:

Neural networks are being increasingly used for problems involving function approximation. However, a key limitation of neural methods is the lack of a measure of how much confidence can be placed in output estimates. In the last few years many authors have addressed this shortcoming from various angles, focusing primarily on predicting output bounds as a function of the trained network's characteristics, typically as defined by the Hessian matrix. In this paper the problem of the effect of e...

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Publication status:
Published

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Publisher copy:
10.1109/72.750542

Authors


Townsend, NW More by this author
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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science, Biomedical Research Centre
Journal:
IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council
Volume:
10
Issue:
2
Pages:
217-230
Publication date:
1999
DOI:
EISSN:
1941-0093
ISSN:
1045-9227
URN:
uuid:f1c88cc1-abf8-4754-b495-0136c9496eb3
Source identifiers:
61538
Local pid:
pubs:61538

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