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dc.contributor.author Van Dyk, E
dc.contributor.author Barnard, E
dc.date.accessioned 2008-01-24T14:14:35Z
dc.date.available 2008-01-24T14:14:35Z
dc.date.issued 2007-11
dc.identifier.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 en
dc.identifier.isbn 978-1-86840-656-2
dc.identifier.uri http://hdl.handle.net/10204/1977
dc.identifier.uri 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:
dc.description 2007: PRASA en
dc.description This paper is published in the South African Computer Journal, Vol 40, pp 37-43
dc.description.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 en
dc.language.iso en en
dc.publisher 18th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA) en
dc.subject Bayesian classifiers en
dc.subject Multinominal features en
dc.title Naive Bayesian classifiers for multinomial features: a theoretical analysis en
dc.type Presentation en


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