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Using Genetic Programming to Extract Knowledge from Artificial Neural Networks

Using Genetic Programming to Extract Knowledge from Artificial Neural Networks

Daniel Rivero, Miguel Varela, Javier Pereira
Copyright: © 2006 |Pages: 25
ISBN13: 9781591409021|ISBN10: 1591409020|ISBN13 Softcover: 9781591409038|EISBN13: 9781591409045
DOI: 10.4018/978-1-59140-902-1.ch006
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MLA

Rivero, Daniel, et al. "Using Genetic Programming to Extract Knowledge from Artificial Neural Networks." Artificial Neural Networks in Real-Life Applications, edited by Juan R. Rabuñal and Julian Dorado, IGI Global, 2006, pp. 116-140. https://doi.org/10.4018/978-1-59140-902-1.ch006

APA

Rivero, D., Varela, M., & Pereira, J. (2006). Using Genetic Programming to Extract Knowledge from Artificial Neural Networks. In J. Rabuñal & J. Dorado (Eds.), Artificial Neural Networks in Real-Life Applications (pp. 116-140). IGI Global. https://doi.org/10.4018/978-1-59140-902-1.ch006

Chicago

Rivero, Daniel, Miguel Varela, and Javier Pereira. "Using Genetic Programming to Extract Knowledge from Artificial Neural Networks." In Artificial Neural Networks in Real-Life Applications, edited by Juan R. Rabuñal and Julian Dorado, 116-140. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-902-1.ch006

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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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