Neural Networks for Conditional Probability Estimation - Forecasting Beyond Point Predictions (Paperback, Softcover reprint of the original 1st ed. 1999)

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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	Perspectives in Neural Computing
Release date:	February 1999
First published:	1999
Authors:	Dirk Husmeier
Dimensions:	235 x 155 x 16mm (L x W x T)
Format:	Paperback
Pages:	275
Edition:	Softcover reprint of the original 1st ed. 1999
ISBN-13:	978-1-85233-095-8
Categories:	Books > Social sciences > Sociology, social studies > Social research & statistics > Social forecasting, futurology Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	1-85233-095-3
Barcode:	9781852330958

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