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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/1977

Title: Naive Bayesian classifiers for multinomial features: a theoretical analysis
Authors: Van Dyk, E
Barnard, E
Keywords: Bayesian classifiers
Multinominal features
Issue Date: Nov-2007
Publisher: 18th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA)
Citation: Van Dyk, E and Barnard, E. 2007. Naive Bayesian classifiers for multinomial features: a theoretical analysis. 18th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Pietermaritzburg, Kwazulu-Natal, South Africa, 28-30 November 2007, pp 6
Abstract: The authors investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density functions for all multinomial likelihood functions describing different classes. They also develop a simplified method to account for the correlation between multinomial variables. With accurate estimates for the distributions of all the likelihood functions, the authors are able to calculate classification error estimates for any such multinomial likelihood classifier. This error estimate can be used for feature selection, since it is easy to predict the effect that different features have on the error rate performance
Description: 2007: PRASA
This paper is published in the South African Computer Journal, Vol 40, pp 37-43
URI: http://hdl.handle.net/10204/1977
http://search.sabinet.co.za/WebZ/images/ejour/comp/comp_v40_a8.pdf:sessionid=0:bad=http://search.sabinet.co.za/ejour/ejour_badsearch.html:portal=ejournal:
ISBN: 978-1-86840-656-2
Appears in Collections:Human language technologies
General science, engineering & technology

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