Neural Networks for Conditional Probability Estimation: Forecasting Beyond Point Predictions

by Dirk Husmeier

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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...

Format:Paperback

Language:English

ISBN:1852330953

ISBN13:9781852330958

Release Date:February 1999

Publisher:Springer

Length:275 Pages

Weight:1.05 lbs.

Dimensions:0.7" x 6.1" x 9.3"

Related Subjects

Computers Computers & Technology Human Vision & Language Systems Pattern Recognition

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Neural Networks for Conditional Probability Estimation: Forecasting Beyond Point Predictions

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