Full metadata record
DC Field | Value | Language |
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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.identifier.uri | http://hdl.handle.net/2003/4962 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-5519 | - |
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.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 | - |
Appears in Collections: | Sonderforschungsbereich (SFB) 475 |
Files in This Item:
File | Description | Size | Format | |
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99_27.pdf | DNB | 492.99 kB | Adobe PDF | View/Open |
tr27-99.ps | 7.5 MB | Postscript | View/Open |
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