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NeuroCOLT
Technical Report NC-TR-95-042
Knowledge
Extraction From Neural Networks : A Survey
R.
Baron
ENS-Lyon CNRS
France
Abstract
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, diff erent techniques for knowledge extraction from
neural networks are presented. Early works have shown the interest
of the study of internal representations, bu t 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.
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