A Comparison between Neural Networks and other Statistical Techniques for Modeling the Relationship between Tobacco and Alcohol and Cancer
نویسندگان
چکیده
Epidemiological data is traditionally analyzed with very simple techniques. Flexible models, such as neural networks, have the potential to discover unanticipated features in the data. However, to be useful, exible models must have e ective control on over tting. This paper reports on a comparative study of the predictive quality of neural networks and other exible models applied to real and arti cial epidemiological data. The results suggest that there are no major unanticipated complex features in the real data, and also demonstrate that MacKay's [1995] Bayesian neural network methodology provides e ective control on over tting while retaining the ability to discover complex features in the arti cial data.
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