dc.contributor.author |
Van Dyk, E
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dc.contributor.author |
Barnard, E
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|
dc.date.accessioned |
2008-01-24T14:14:35Z |
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dc.date.available |
2008-01-24T14:14:35Z |
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dc.date.issued |
2007-11 |
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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 |
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dc.identifier.uri |
http://hdl.handle.net/10204/1977
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|
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:
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dc.description |
2007: PRASA |
en |
dc.description |
This paper is published in the South African Computer Journal, Vol 40, pp 37-43 |
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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 |
Conference Presentation |
en |
dc.identifier.apacitation |
Van Dyk, E., & Barnard, E. (2007). Naive Bayesian classifiers for multinomial features: a theoretical analysis. 18th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA). http://hdl.handle.net/10204/1977 |
en_ZA |
dc.identifier.chicagocitation |
Van Dyk, E, and E Barnard. "Naive Bayesian classifiers for multinomial features: a theoretical analysis." (2007): http://hdl.handle.net/10204/1977 |
en_ZA |
dc.identifier.vancouvercitation |
Van Dyk E, Barnard E, Naive Bayesian classifiers for multinomial features: a theoretical analysis; 18th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA); 2007. http://hdl.handle.net/10204/1977 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Van Dyk, E
AU - Barnard, E
AB - 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
DA - 2007-11
DB - ResearchSpace
DP - CSIR
KW - Bayesian classifiers
KW - Multinominal features
LK - https://researchspace.csir.co.za
PY - 2007
SM - 978-1-86840-656-2
T1 - Naive Bayesian classifiers for multinomial features: a theoretical analysis
TI - Naive Bayesian classifiers for multinomial features: a theoretical analysis
UR - http://hdl.handle.net/10204/1977
ER -
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en_ZA |