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- R. Herbrich, M. Keilbach,
T. Graepel, P. Bollmann-Sdorra, and K. Obermayer. Neural Networks in
Economics: Background, Applications and New Developments.
.
In Advances in Computational Economics: Computational Techniques for
Modelling Learning in Economics, volume 11, pages 169-196. Kluwer
Academics, 1999.
(FTP Gzipped PostScript, 27 pages, 102 kb)
Neural Networks were developed in the sixties as devices for
classification and regression. The approach was originally inspired from
Neuroscience. Its attractiveness lies in the ability to learn, i.e. to
generalize to as yet unseen observations. One aim of this paper is to give an
introduction to the technique of Neural Networks and an overview of the most
popular architectures. We start from statistical learning theory to introduce
the basics of learning. Then, we give an overview of the general principles
of neural networks and of their use in the field of Economics. A second
purpose is to introduce a recently developed Neural Network Learning
technique, so called Support Vector Network Learning, which is an application
of ideas from statistical learning theory. This approach has shown very
promising results on problems with a limited amount of training examples.
Moreover, utilizing a technique that is known as the kernel trick, Support
Vector Networks can easily be adapted to nonlinear models. Finally, we
present an economic application of this approach from the field of preference
learning.
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