Extracting Knowledge from Neural Networks

Christie M. Fuller; Rick L. Wilson

doi:10.4018/978-1-59904-931-1.ch031

Extracting Knowledge from Neural Networks

Christie M. Fuller
, Rick L. Wilson

Information Technology and Supply Chain Management Department

Oklahoma State University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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.).

Original language	English
Title of host publication	Encyclopedia of Knowledge Management
Subtitle of host publication	Second Edition: Volume I
Pages	320-330
Number of pages	11
ISBN (Electronic)	9781599049328
DOIs	https://doi.org/10.4018/978-1-59904-931-1.ch031
State	Published - 1 Jan 2010

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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

Abstract

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