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Abstract: A survey of several well known rule extraction techniques is presented in my report, in the light of a broader paradigm of connectionist-symbolic learning. In the first part of the report I have covered some introductory aspects about machine learning, and investigated the reasons for a possible connectionist-symbolic integration, thereby presenting a hybrid learning framework. Within this hybrid learning framework, my report focuses on the survey of Rule extraction techniques from Trained... (Update)
Context of citations to this paper: More
.... in the sense that they can also deal with noisy data and also fulfill some of the nice properties like expressivity, translucency etc [20]. However, the issue of comprehensibility is not so well addressed. Even the very best and the latest work by [21] fails to reduce the...
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BibTeX entry: (Update)
Ashish Darbari. Rule Extraction from Trained ANN: A Survey. Tech- nical report, Institute of Artificial Intelligence, Dept. of Computer Science, TU Dresden, Germany, 2000. http://citeseer.ist.psu.edu/darbari01rule.html More
@techreport{ darbari00rule,
author = "A. Darbari",
title = "Rule Extraction from Trained {ANN}: {A} Survey",
institution = "Institute of Artificial Intelligence, Dept. of Computer Science,
TU Dresden, Germany",
year = "2000",
url = "citeseer.ist.psu.edu/darbari01rule.html" }
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