Extracting Knowledge from Neural Networks

Extracting Knowledge from Neural Networks

Christie M. Fuller (Oklahoma State University, USA) and Rick L. Wilson (Oklahoma State University, USA)
Copyright: © 2006 |Pages: 9
DOI: 10.4018/978-1-59140-573-3.ch025
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Abstract

Neural networks (NN) as classifier systems have shown great promise in many problem domains in empirical studies over the past two decades. Using case classification accuracy as the criteria, neural networks have typically outperformed traditional parametric techniques (e.g., discriminant analysis, logistic regression) as well as other non-parametric approaches (e.g., various inductive learning systems such as ID3, C4.5, CART, etc.).

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