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The Development of the AQ20 Learning System and Initial Experiments

Show simple item record Michalski, Ryszard S. Cervone, Guido Panait, Liviu A. 2006-11-03T18:17:13Z 2006-11-03T18:17:13Z 2001- en_US
dc.identifier.citation Cervone, G., Panait, L. A. and Michalski, R. S., "The Development of the AQ20 Learning System and Initial Experiments," Tenth International Symposium on Intelligent Information Systems, Zakopane, Poland, June, 2001. en_US
dc.description.abstract Research on a new system implementing the AQ learning methodology, called AQ20, is briefly described, and illustrated by initial results from an experimental version. Like its predecessors, AQ20 is a multi-purpose learning system for inducing general concepts descriptions from concept examples and counter-examples. AQ20 is viewed as a natural induction system because it aims at producing descriptions that are not only accurate but also easy to understand and interpret. This feature is achieved by representing descriptions in the form of attributional rulesets that have a higher representation power than decision trees or conventional decision rules. Among new features implemented in AQ20 are the ability to handle continuous variables without prior discretization, to control the degree of generality of rules by a continuous parameter, and to generate more than one rule from a star. Initial experimental results from applying AQ20 to selected problems in the UCI repository demonstrate a high utility of the new system.
dc.description.sponsorship This research has been supported in part by the National Science Foundation under grant IIS-9906858 and by a UMBC Grant under LUCITE Task #32. en_US
dc.format.extent 2374 bytes
dc.format.extent 271303 bytes
dc.format.extent 99390 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 01-3 en_US
dc.subject symbolic learning en_US
dc.subject natural induction en_US
dc.title The Development of the AQ20 Learning System and Initial Experiments en_US
dc.type Presentation en_US

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