Bounds for the Bayes error in classification: A Bayesian approach using discriminant analysis

dc.contributor.author	Pham-Gia T.	en
dc.contributor.author	Turkkan N.	en
dc.contributor.author	Bekker A.	en
dc.date.accessioned	2012-11-01T16:31:28Z
dc.date.available	2012-11-01T16:31:28Z
dc.date.issued	2007	en
dc.identifier.citation	Statistical Methods and Applications	en
dc.identifier.citation	16	en
dc.identifier.citation	1	en
dc.identifier.issn	16182510	en
dc.identifier.other	10.1007/s10260-006-0012-x	en
dc.identifier.uri	http://hdl.handle.net/10500/7280
dc.description.abstract	We study two of the classical bounds for the Bayes error P e , Lissack and Fu's separability bounds and Bhattacharyya's bounds, in the classification of an observation into one of the two determined distributions, under the hypothesis that the prior probability χ itself has a probability distribution. The effectiveness of this distribution can be measured in terms of the ratio of two mean values. On the other hand, a discriminant analysis-based optimal classification rule allows us to derive the posterior distribution of χ, together with the related posterior bounds of P e. © Springer-Verlag 2007.	en
dc.language.iso	en	en
dc.subject	Bernoulli; Beta distribution; Bhattacharyya bounds; Discriminant analysis; Hypergeometric functions; Lissack and Fu bounds; Misclassification; Overlapping coefficient	en
dc.title	Bounds for the Bayes error in classification: A Bayesian approach using discriminant analysis	en
dc.type	Article	en