Neural Networks for Conditional Probability Estimation

Language: English

Published by Springer, Springer Feb 1999, 1999

1852330953 / 9781852330958

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This item is printed on demand - Print on Demand Titel. Neuware -Conventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is to predict the future value of some entity of interest on the basis of a time series of past measurements or observations. Typical training schemes aim to minimise the sum of squared deviations between predicted and actual values (the 'targets'), by which, ideally, the network learns the conditional mean of the target given the input. If the underlying conditional distribution is Gaus sian or at least unimodal, this may be a satisfactory approach. However, for a multimodal distribution, the conditional mean does not capture the relevant features of the system, and the prediction performance will, in general, be very poor. This calls for a more powerful and sophisticated model, which can learn the whole conditional probability distribution. Chapter 1 demonstrates that even for a deterministic system and 'be nign' Gaussian observational noise, the conditional distribution of a future observation, conditional on a set of past observations, can become strongly skewed and multimodal. In Chapter 2, a general neural network structure for modelling conditional probability densities is derived, and it is shown that a universal approximator for this extended task requires at least two hidden layers. A training scheme is developed from a maximum likelihood approach in Chapter 3, and the performance ofthis method is demonstrated on three stochastic time series in chapters 4 and 5.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 300 pp. Englisch.

Seller Inventory # 9781852330958

Title
Neural Networks for Conditional Probability Estimation
Author
Dirk Husmeier
Publisher
Springer, Springer Feb 1999
Publication year
1999
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
1852330953
ISBN 13
9781852330958
Item weight
458 grams
Dimensions
235x155x17 mm

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