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https://hdl.handle.net/10419/22204
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bühlmann, Peter | en |
dc.date.accessioned | 2009-01-29T14:54:22Z | - |
dc.date.available | 2009-01-29T14:54:22Z | - |
dc.date.issued | 2004 | - |
dc.identifier.uri | http://hdl.handle.net/10419/22204 | - |
dc.description.abstract | Ensemble 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.iso | eng | en |
dc.publisher | |aHumboldt-Universität zu Berlin, Center for Applied Statistics and Economics (CASE) |cBerlin | en |
dc.relation.ispartofseries | |aPapers |x2004,31 | en |
dc.subject.ddc | 330 | en |
dc.title | Bagging, boosting and ensemble methods | - |
dc.type | Working Paper | en |
dc.identifier.ppn | 495308447 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
dc.identifier.repec | RePEc:zbw:caseps:200431 | en |
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