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Probing for Sparse and Fast Variable Selection with Model-Based Boosting
We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to fit a statistical model while performing variable selection at the same time. A drawback of the fitting lies in the need of multiple model fits on slig...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Hindawi
2017
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5555005/ https://www.ncbi.nlm.nih.gov/pubmed/28831289 http://dx.doi.org/10.1155/2017/1421409 |