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Knowledge Extraction From Neural Networks : A Survey

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dc.contributor.author Laboratoire de l'informatique du parallélisme en_US
dc.contributor.author Baron, Richard en_US
dc.date.accessioned 2007-05-24T11:53:16Z
dc.date.available 2007-05-24T11:53:16Z
dc.date.issued 1994-05-27 en_US
dc.identifier.other LIP-RR - 1994-17 en_US
dc.identifier.uri http://hdl.handle.net/2332/1191
dc.description.abstract (eng) Artificial neural networks may learn to solve arbitrary complex problems. But knowledge acquired is hard to exhibit. Thus neural networks appear as ``black boxes'', the decisions of which can't be explained. In this survey, different techniques for knowledge extraction from neural networks are presented. Early works have shown the interest of the study of internal representations, but these studies were domain specific. Thus, authors tried to extract a more general form of knowledge, like rules of an expert system. In a more restricted field, it is also possible to extract automata from neural networks, likely to recognize a formal language. Finally, numerical information may be obtained in process modelling, and this may be of interest in industrial applications. en_US
dc.format.extent 2+13p en_US
dc.format.extent 244149 bytes
dc.format.extent 23 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/octet-stream
dc.language.iso eng en_US
dc.rights http://lara.inist.fr/utilisation.jsp en_US
dc.source.uri ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/RR/RR1994/RR1994-17.ps.Z en_US
dc.subject Artificial Neural Networks en_US
dc.subject Knowledge Extraction en
dc.subject Expert Systems en
dc.title Knowledge Extraction From Neural Networks : A Survey en_US
dc.type Research report en_US

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