Contribution to Book
Extracting Knowledge From Neural Networks
Knowledge Management: Concepts, Methodologies, Tools and Applications
(2008)
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.).
Disciplines
Publication Date
2008
Editor
Murray E. Jennex
Publisher
IGI Global
ISBN
9781599049335
DOI
10.4018/978-1-59904-933-5.ch062
Citation Information
Christie M. Fuller and Rick L. Wilson. "Extracting Knowledge From Neural Networks" Hershey, PAKnowledge Management: Concepts, Methodologies, Tools and Applications Vol. 2 (2008) p. 748 - 757 Available at: http://works.bepress.com/christie-fuller/7/