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dc.contributor.authorFrank, Eibe
dc.contributor.authorHuber, Klaus-Perter
dc.coverage.spatialConference held at Aachenen_NZ
dc.date.accessioned2008-12-01T03:51:49Z
dc.date.available2008-12-01T03:51:49Z
dc.date.issued1996
dc.identifier.citationFrank, E. & Huber, K.-P. (1996). Active learning of soft rules for system modelling. Paper presented at 2nd European Congress on Intelligent Techniques and Soft Computing, Aachen, September 2-5, 1996.en_US
dc.identifier.urihttps://hdl.handle.net/10289/1505
dc.description.abstractUsing rule learning algorithms to model systems has gained considerable interest in the past. The underlying idea of active learning is to learning algorithm influence the selection of training examples. The presented method estimates the utility of new experiments based on the knowledge represented by the existing rulebase. An extended rule format allows to deal with uncertainty. Experiments with different artificial system functions show that the presented method improves the model quality respectively decreases the number of experiments needed to reach a specific level of performance.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.source2nd European Congress on Intelligent Techniques and Soft Computingen_NZ
dc.subjectcomputer scienceen_US
dc.subjectrule learningen_US
dc.subjectactive learningen_US
dc.titleActive learning of soft rules for system modellingen_US
dc.typeConference Contributionen_US
pubs.elements-id18444
pubs.finish-date1996-09-05en_NZ
pubs.start-date1996-09-02en_NZ


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