Skip to main content


eCommons@Cornell

eCommons@Cornell >
College of Engineering >
Computer Science >
Computer Science Technical Reports >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/5940
Title: Numerical Approximations to Expectations of Functions of Binary Sequences Subject to Error
Authors: Jackson, D.M.
White, L.J.
Keywords: computer science
technical report
Issue Date: Nov-1970
Publisher: Cornell University
Citation: http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR70-82
Abstract: There 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.
URI: http://hdl.handle.net/1813/5940
Appears in Collections:Computer Science Technical Reports

Files in This Item:

File Description SizeFormat
70-82.pdf1.25 MBAdobe PDFView/Open
70-82.ps421.53 kBPostscriptView/Open

Refworks Export

Items in eCommons are protected by copyright, with all rights reserved, unless otherwise indicated.

 

© 2014 Cornell University Library Contact Us