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Experiments with Generalized Binary Probabilistic Independence Model

dc.contributor.authorWong, S. K. M.en_US
dc.contributor.authorYao, Y. Y.en_US
dc.date.accessioned2007-04-23T17:43:08Z
dc.date.available2007-04-23T17:43:08Z
dc.date.issued1989-04en_US
dc.description.abstractThis paper reports experiments with the generalized binary probabilistic independence model in which more complete statistical information is used. Two basic sets of experiments, referred to as nonpredictive and predictive, have been performed on two standard test collections. In the nonpredictive experiments, the complete relevance information was used to determine the optimal performance of the model. On the other hand, in the predictive experiments only partial relevance information was used to demonstrate the predictive power of the model. Although a simple method for estimating the parameters was used in the designed tests, significant improvements were obtained for both test collections. These preliminary results suggest that further work on the generalized model is worthwhile.en_US
dc.format.extent1013524 bytes
dc.format.extent242247 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR89-988en_US
dc.identifier.urihttps://hdl.handle.net/1813/6904
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleExperiments with Generalized Binary Probabilistic Independence Modelen_US
dc.typetechnical reporten_US

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