Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/36602 
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRüping, Stefanen
dc.contributor.authorWeihs, Clausen
dc.date.accessioned2009-05-27-
dc.date.accessioned2010-07-15T10:07:54Z-
dc.date.available2010-07-15T10:07:54Z-
dc.date.issued2009-
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
dc.language.isoengen
dc.publisher|aTechnische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen |cDortmunden
dc.relation.ispartofseries|aTechnical Report |x2009,02en
dc.subject.ddc519en
dc.titleKernelized design of experiments-
dc.typeWorking Paperen
dc.identifier.ppn600486184en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:sfb475:200902en

Files in This Item:
File
Size
125.82 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.