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Papers, CASE - Center for Applied Statistics and Economics, HU Berlin >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10419/22204
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Full metadata record
| DC Field | | Value | | Language |
| dc.contributor.author | | Bühlmann, Peter | | en_US |
| dc.date.accessioned | | 2009-01-29T14:54:22Z | | - |
| dc.date.available | | 2009-01-29T14:54:22Z | | - |
| dc.date.issued | | 2004 | | en_US |
| 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_US |
| dc.language.iso | | eng | | en_US |
| dc.publisher | | | | en_US |
| dc.relation.ispartofseries | | Papers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,31 | | en_US |
| dc.subject.ddc | | 330 | | en_US |
| dc.title | | Bagging, boosting and ensemble methods | | en_US |
| dc.type | | Working Paper | | en_US |
| dc.identifier.ppn | | 495308447 | | en_US |
| dc.rights | | http://www.econstor.eu/dspace/Nutzungsbedingungen | | - |
| dc.identifier.repec | | RePEc:zbw:caseps:200431 | | - |
| Appears in Collections: | | Papers, CASE - Center for Applied Statistics and Economics, HU Berlin
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