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Estimating regional noise on neural network predictions

✍ Scribed by Karsten E. Weber; Werner Schlagner; Knuth Schweier

Book ID
104161705
Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
185 KB
Volume
36
Category
Article
ISSN
0031-3203

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✦ Synopsis

A new method for estimating the variance of noise for nonlinear regression is presented. The noise is modelled to be regional, i.e. its variance depends on the input, and it consists of two sources: measurement errors and inherent noise of the underlying function. Our approach consists of two neural networks using Bayesian methods, which are trained in sequence. It is orientated by the assumption of unbiased predictions of the mean and the conΓΏdence of network prognoses, which are used to predict the variance of noise. We demonstrate our approach on two toy and one real data sets.

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