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Ensemble learning by data resampling
We investigate ensemble learning methods that construct a classifier ensemble by repeatedly sampling the original training data and building a member classifier from each subsample. We find that the performance of standard Bagging can frequently be improved upon by simple variations of the sampling...
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| Autor principal: | |
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| Formato: | Online |
| Idioma: | en |
| Publicado em: |
ResearchSpace@Auckland
2004
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| Acesso em linha: | http://hdl.handle.net/2292/996 |
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