Neural networks for conditional probability estimation : forecasting beyond point predictions /
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 pa...
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| Format: | eBook |
| Language: | English |
| Published: |
London ; New York :
Springer,
[1999]
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| Series: | Perspectives in neural computing.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (xxiii, 275 pages) : illustrations. |
| Bibliography: | Includes bibliographical references (pages [267]-272) and index. |
| ISBN: | 9781447108474 (electronic bk.) 1447108477 (electronic bk.) |