Reasoning with Meta-values in AQ Learning

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dc.contributor.authorMichalski, Ryszard S.
dc.contributor.authorWojtusiak, Janusz
dc.date.accessioned2006-11-03T18:17:30Z
dc.date.available2006-11-03T18:17:30Z
dc.date.issued2005-06
dc.description.abstractThis paper describes methods for reasoning with missing, irrelevant and not applicable meta-values in the AQ attributional rule learning. The methods address issues of handling these values in datasets both for rule learning and rule testing. In rule learning, the presence of these values affects the extension-against generalization operator in star generation, and the rule matching operator. In rule testing, these values affect the execution of the rule matching operator. The presented methods have been implemented in the AQ21 learning program and tested on four datasets.
dc.format.extent1809 bytes
dc.format.extent162040 bytes
dc.format.mimetypetext/xml
dc.format.mimetypeapplication/pdf
dc.identifier.citationMichalski, R. S. and Wojtusiak, J., "Reasoning with Meta-values in AQ Learning," Reports of the Machine Learning and Inference Laboratory, MLI 05-1, George Mason University, Fairfax, VA, June, 2005.
dc.identifier.urihttps://hdl.handle.net/1920/1491
dc.language.isoen_US
dc.relation.ispartofseriesP 05-2
dc.relation.ispartofseriesMLI 05-1
dc.subjectMachine learning
dc.subjectConcept learning
dc.subjectAQ learning
dc.subjectMeta-values
dc.titleReasoning with Meta-values in AQ Learning
dc.typeTechnical report

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