When Stopping Rules Don't Stop

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dc.contributorVirginia Tech. Department of Computer Science. Digital Library Research Laboratoryen
dc.contributor.authorFrance, Robert K.en
dc.contributor.departmentDigital Library Research Laboratoryen
dc.contributor.departmentComputer Scienceen
dc.date.accessed2014-09-04en
dc.date.accessioned2015-05-29T20:36:37Zen
dc.date.available2015-05-29T20:36:37Zen
dc.date.issued1995en
dc.description.abstractPerforming ranked retrieval on large document collections can be slow. The method of stopping rules has been proposed to make it more efficient. Stopping rules, which terminate search when the highest ranked documents have been determined to some degree of likelihood, are attractive and have proven useful in clustering, but have not worked well in retrieval experiments. This paper presents a statistical analysis of why they have failed and where they can be expected to continue failing.en
dc.format.extent23 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationFrance, Robert K. "When Stopping Rules Don't Stop." Internal Report, Virginia Tech, 1995.en
dc.identifier.urihttp://hdl.handle.net/10919/52844en
dc.identifier.urlhttp://www.dlib.vt.edu/reports/StoppingRules.pdfen
dc.language.isoen_USen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectStopping rulesen
dc.subjectDocument collectionsen
dc.subjectRanked retrievalen
dc.titleWhen Stopping Rules Don't Stopen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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