You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
| You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
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Table of Contents  Multivariate Data Modeling Neural Networks Mapping of Spaces |
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| See also: introduction to ANNs |   |
Newcomers in the field of multivariate data analysis and neural networks often think that an artificial neural network (ANN) is a type of a black magic box which can be used to enter data and get a solution back. Although this view is potentially dangerous, there is also a grain of truth in it. We could therefore regard an ANN as an abstract machine which creates a non-linear mapping between an n-dimensional input data space and a p-dimension output space. n is usually much larger than p; with p often being in the range of 1 to 3 (since the human interpreter is restricted to a maximum dimensionality of 3 or perhaps 4).
This non-linear mapping is set up during the learning process of a neural
network. The "art" of training a neural network is to control the training
in such a way that the resulting mapping represents the underlying relationship
within the data, avoiding any adjustment for noise, or errors in the data.
Last Update: 2005-Jul-16