Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks

Book chapter


Dybowski, Richard and Roberts, Stephen J. 2001. Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks. in: Dybowski, R. and Gant, V. (ed.) Clinical Applications of Artificial Neural Networks Cambridge University Press. pp. 298-326
AuthorsDybowski, Richard and Roberts, Stephen J.
EditorsDybowski, R. and Gant, V.
Abstract

The chapter opens with an introduction to regression and its implementation within the maximum-likelihood framework. This is followed by a general introduction to classical confidence intervals and prediction intervals. We set the scene by first considering confidence and prediction intervals based on univariate samples, and then we progress to regarding these intervals in the context of linear regression and logistic regression. Since a feed-forward neural network is a type of regression model, the concepts of confidence and prediction intervals are applicable to these networks, and we look at several techniques for doing this via maximum-likelihood estimation. An alternative
to the maximum-likelihood framework is Bayesian statistics, and we examine the notions of Bayesian confidence and predictions intervals as applied to feed-forward networks. This includes a critique on Bayesian confidence intervals and classification.

Keywordsartificial neural networks; medicine; Bayesian statistics; regression analysis
Book titleClinical Applications of Artificial Neural Networks
Page range298-326
Year2001
PublisherCambridge University Press
Publication dates
Print2001
Publication process dates
Deposited02 Nov 2009
ISBN0511339941
Web address (URL)http://www.cambridge.org/catalogue/catalogue.asp?isbn=0511339941
http://hdl.handle.net/10552/364
Additional information

Citation:
Dybowski, R., Roberts, S. J. (2001) ‘Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks’ In Dybowski R. & Gant ,V. (eds.), Clinical Applications of Artificial Neural Networks, Cambridge University Press 2001, pp 298-326.

Accepted author manuscript
License
CC BY-ND
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