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Using Genetic Programming to Extract Knowledge from Artificial Neural Networks
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Author(s): Daniel Rivero (University of A Coruna, Spain), Miguel Varela (University of A Coruna, Spain)and Javier Pereira (University of A Coruna, Spain)
Copyright: 2006
Pages: 25
Source title:
Artificial Neural Networks in Real-Life Applications
Source Author(s)/Editor(s): Juan R. Rabuñal (University of A Coruña, Spain)and Julian Dorado (University of A Coruña, Spain)
DOI: 10.4018/978-1-59140-902-1.ch006
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
A technique is described in this chapter that makes it possible to extract the knowledge held by previously trained artificial neural networks. This makes it possible for them to be used in a number of areas (such as medicine) where it is necessary to know how they work, as well as having a network that functions. This chapter explains how to carry out this process to extract knowledge, defined as rules. Special emphasis is placed on extracting knowledge from recurrent neural networks, in particular when applied in predicting time series.
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