Direct Minimization of Error Rates in Multivariate Classification

dc.contributor.author	Röhl, Michael C.	de
dc.contributor.author	Weihs, Claus	de
dc.date.accessioned	2004-12-06T18:39:57Z
dc.date.available	2004-12-06T18:39:57Z
dc.date.issued	1999	de
dc.description.abstract	We propose a computer intensive method for linear dimension reduction which minimizes the classification error directly. Simulated annealing (Bohachevsky et al. 1986) as a modern optimization technique is used to solve this problem effectively. This approach easily allows to incorporate user requests by means of penalty terms. Simulations demonstrate the superiority of optimal classification to classical discriminant analysis (McLachlan 1992). Special emphasis is put on the case when discriminant analysis collapses.	en
dc.format.extent	228863 bytes
dc.format.extent	239657 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4933
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-7033
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	classification	en
dc.subject	discriminant analysis	en
dc.subject	error rate	en
dc.subject	simulated annealing	en
dc.subject	user requests	en
dc.subject.ddc	310	de
dc.title	Direct Minimization of Error Rates in Multivariate Classification	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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