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Numerical Approximations to Expectations of Functions of Binary Sequences Subject to Error

dc.contributor.authorJackson, D.M.en_US
dc.contributor.authorWhite, L.J.en_US
dc.date.accessioned2007-04-19T17:55:17Z
dc.date.available2007-04-19T17:55:17Z
dc.date.issued1970-11en_US
dc.description.abstractThere is growing interest in devising non-statistical classification algorithms for multivariate populations. Statistical algorithms are avoided either because they are too costly, or because an adequate statistical model for the population does not exist (e.g. use of trainable linear machines in pattern recognition). Such algorithms may be sensitive (unstable) to errors in their data. The particular case of populations of objects characterised by binary attributes susceptible to independent and equiprobable errors is examined. The determination of stability requires the prior computation of the expectation of a statistical function of the object-pair similarities. The order and convergence of a numerical approximation for determining these expectations with prescribed accuracy is examined in the sub-asymptotic case in which normality does not occur. A number of results are given.en_US
dc.format.extent1275301 bytes
dc.format.extent431651 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR70-82en_US
dc.identifier.urihttps://hdl.handle.net/1813/5940
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleNumerical Approximations to Expectations of Functions of Binary Sequences Subject to Erroren_US
dc.typetechnical reporten_US

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