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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: 2011
Pages: 11
Source title:
Encyclopedia of Knowledge Management, Second Edition
Source Author(s)/Editor(s): David Schwartz (Bar-Ilan University, Israel)and Dov Te'eni (Tel-Aviv University , Israel)
DOI: 10.4018/978-1-59904-931-1.ch031
PurchaseView on the publisher's website for pricing and purchasing information.
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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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