Neural nets - their use and abuse for small data sets
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Date
2000
DOI
Open Access Location
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Publisher
Massey University
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Abstract
Neural nets can be used for non-linear classification and regression models. They have a big advantage
over conventional statistical tools in that it is not necessary to assume any mathematical form for the
functional relationship between the variables. However, they also have a few associated problems chief of
which are probably the risk of over-parametrization in the absence of P-values, the lack of appropriate
diagnostic tools and the difficulties associated with model interpretation. The first of these problems is
particularly important in the case of small data sets. These problems are investigated in the context of real
market research data involving non-linear regression and discriminant analysis. In all cases we compare
the results of the non-linear neural net models with those of conventional linear statistical methods. Our
conclusion is that the theory and software for neural networks has some way to go before the above
problems will be solved.
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Keywords
Neural nets, Statistical tools, Data sets, Research data, Datasets
Citation
Meyer, D. (2000), Neural nets - their use and abuse for small data sets, Research Letters in the Information and Mathematical Sciences, 1, 145-158