Neural networks for conditional probability estimation: forecasting beyond point predictions

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Springer London, Limited, 1999 - Computers - 275 pages
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This volume presents a neural network architecture for the prediction of conditional probability densities - which is vital when carrying out universal approximation on variables which are either strongly skewed or multimodal. Two alternative approaches are discussed: the GM network, in which all parameters are adapted in the training scheme, and the GM-RVFL model which draws on the random functional link net approach. Points of particular interest are: - it examines the modification to standard approaches needed for conditional probability prediction; - it provides the first real-world test results for recent theoretical findings about the relationship between generalisation performance of committees and the over-flexibility of their members; This volume will be of interest to all researchers, practitioners and postgraduate / advanced undergraduate students working on applications of neural networks - especially those related to finance and pattern recognition.

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Contents

The Bayesian Evidence Scheme for Regularisation 147
18
A Universal Approximator Network for Predicting Condi
21
A Maximum Likelihood Training Scheme
39
Copyright

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About the author (1999)

Husmeier, Imperial College, London, UK.

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