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Kompletter Metadatensatz
DublinCore-Feld | Wert | Sprache |
---|---|---|
dc.contributor.author | Rüping, Stefan | en |
dc.contributor.author | Weihs, Claus | en |
dc.date.accessioned | 2009-05-27 | - |
dc.date.accessioned | 2010-07-15T10:07:54Z | - |
dc.date.available | 2010-07-15T10:07:54Z | - |
dc.date.issued | 2009 | - |
dc.identifier.uri | http://hdl.handle.net/10419/36602 | - |
dc.description.abstract | This paper describes an approach for selecting instances in regression problems in the cases where observations x are readily available, but obtaining labels y is hard. Given a database of observations, an algorithm inspired by statistical design of experiments and kernel methods is presented that selects a set of k instances to be chosen in order to maximize the prediction performance of a support vector machine. It is shown that the algorithm significantly outperforms related approaches on a number of real-world datasets. | en |
dc.language.iso | eng | en |
dc.publisher | |aTechnische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen |cDortmund | en |
dc.relation.ispartofseries | |aTechnical Report |x2009,02 | en |
dc.subject.ddc | 519 | en |
dc.title | Kernelized design of experiments | - |
dc.type | Working Paper | en |
dc.identifier.ppn | 600486184 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
dc.identifier.repec | RePEc:zbw:sfb475:200902 | en |
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