Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/22204 
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dc.contributor.authorBühlmann, Peteren
dc.date.accessioned2009-01-29T14:54:22Z-
dc.date.available2009-01-29T14:54:22Z-
dc.date.issued2004-
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
dc.language.isoengen
dc.publisher|aHumboldt-Universität zu Berlin, Center for Applied Statistics and Economics (CASE) |cBerlinen
dc.relation.ispartofseries|aPapers |x2004,31en
dc.subject.ddc330en
dc.titleBagging, boosting and ensemble methods-
dc.typeWorking Paperen
dc.identifier.ppn495308447en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:caseps:200431en

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