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Neural Networks in Economics: Background, Applications and New Developments
Citation key Herbrich1999b
Author Herbrich, R. and Keilbach, M. and Graepel, T. and Bollmann-Sdorra, P. and Obermayer, K.
Title of Book Advances in Computational Economics: Computational Techniques for Modelling Learning in Economics
Pages 169 – 196
Year 1999
Volume 11
Editor Brenner, T.
Publisher Kluwer Academics
Abstract 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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