Regularization and Model Selection in the Context of Density Estimation

dc.contributor.author	Kreutz, Martin	de
dc.contributor.author	Reimetz, Anja M.	de
dc.contributor.author	Seelen, Werner von	de
dc.contributor.author	Sendhoff, Bernhard	de
dc.contributor.author	Weihs, Claus	de
dc.date.accessioned	2004-12-06T18:40:37Z
dc.date.available	2004-12-06T18:40:37Z
dc.date.issued	1999	de
dc.description.abstract	We propose a new information theoretically based optimization criterion for the estimation of mixture density models and compare it with other methods based on maximum likelihood and maximum a posterio estimation. For the optimization, we employ an evolutionary algorithm which estimates both structure and parameters of the model. Experimental results show that the chosen approach compares favourably with other methods for estimation problems with few sample data as well as for problems where the underlying density is non-stationary.	en
dc.format.extent	504819 bytes
dc.format.extent	7682052 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4962
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-5519
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject.ddc	310	de
dc.title	Regularization and Model Selection in the Context of Density Estimation	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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