Extracting Knowledge from Neural Networks

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Author(s): Christie M. Fuller (Oklahoma State University, USA)and Rick L. Wilson (Oklahoma State University, USA)
Copyright: 2008
Pages: 10
Source title: Knowledge Management: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Murray E. Jennex (San Diego State University, USA)
DOI: 10.4018/978-1-59904-933-5.ch062

Keywords: Information Science Reference / Knowledge Management / Library & Information Science

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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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Extracting Knowledge from Neural Networks

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