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The Computer Journal 1972 15(3):214-221; doi:10.1093/comjnl/15.3.214
© 1972 by British Computer Society
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Stability problems in non-statistical classification theory

D. M. Jackson1 ¶ * and L. J. White2 §

1 Department of Computer Science, Cornell University, Ithaca, New York, USA, 2 Department of Computer and Information Science, The Ohio State University, Columbus, Ohio, USA

There is growing interest in devising non-statistical classification algorithms for multivariate populations. Such algorithms may be sensitive to errors in their data. The particular case of populations of objects characterised by binary attributes susceptible to independent and equiprobable errors is examined. A computationally viable technique is developed which determines the expectation of arbitrary statistical functions of arbitrary similarity functions in the presence of such errors. The convergence of a numerical approximation for determining these expectations with prescribed accuracy is proved, and this accuracy related to the termination rule of the numerical algorithm. The time reduction obtained with the numerical method is also discussed, and computational experience with the technique is cited.

Received March 1971.

* Department of Computer Science, Cornell University, Ithaca, New York, 14850, USA.

§ Department of Computer and Information Science, The Ohio State University, Columbus, Ohio 43210, USA.

Present address: Department of Applied Analysis and Computer Science, University of Waterloo, Waterloo, Ontario, Canada.


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