Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22204
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBühlmann, Peteren_US
dc.date.accessioned2009-01-29T14:54:22Z-
dc.date.available2009-01-29T14:54:22Z-
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/10419/22204-
dc.description.abstractEnsemble methods aim at improving the predictive performance of a given statistical learning or model fitting technique. The general principleof ensemble methods is to construct a linear combinationof some model fitting methods, instead of using a single fit of the method.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseries|aPapers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) |x2004,31en_US
dc.subject.ddc330en_US
dc.titleBagging, boosting and ensemble methodsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn495308447en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:caseps:200431-

Files in This Item:
File
Size
5.17 MB





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