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dc.contributor.authorRüping, Stefanen_US
dc.contributor.authorWeihs, Clausen_US
dc.date.accessioned2009-05-27en_US
dc.date.accessioned2010-07-15T10:07:54Z-
dc.date.available2010-07-15T10:07:54Z-
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/10419/36602-
dc.description.abstractThis 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_US
dc.language.isoengen_US
dc.publisherUniv., SFB 475 Dortmunden_US
dc.relation.ispartofseriesTechnical Report // Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 2009,02en_US
dc.subject.ddc310en_US
dc.titleKernelized design of experimentsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn600486184en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:sfb475:200902-
Appears in Collections:Technical Reports, SFB 475: Komplexitätsreduktion in multivariaten Datenstrukturen, TU Dortmund

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