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dc.contributor.authorSegre, Alberto M.en_US
dc.contributor.authorElkan, Charles P.en_US
dc.contributor.authorRussell, Alexen_US
dc.date.accessioned2007-04-23T17:47:32Z
dc.date.available2007-04-23T17:47:32Z
dc.date.issued1990-05en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR90-1126en_US
dc.identifier.urihttps://hdl.handle.net/1813/6966
dc.description.abstractA number of experimental evaluations of explanation-based learning (EBL) have appeared in the literature on machine learning. Closer examination of experimental methodologies used in the past reveals certain methodological flaws that call into question the conclusions drawn from these experiments. This paper illustrates some of the more common methodological problems, proposes a novel experimental framework for future empirical studies of EBL, and presents an example of an experiment performed within this new framework.en_US
dc.format.extent2348741 bytes
dc.format.extent570900 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleOn Valid and Invalid Methodologies for Experimentala Evaluations of EBLen_US
dc.typetechnical reporten_US


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