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Covariance-regularized regression and classification for high-dimensional problems
In recent years, many methods have been developed for regression in high-dimensional settings. We propose covariance-regularized regression, a family of methods that use a shrunken estimate of the inverse covariance matrix of the features in order to achieve superior prediction. An estimate of the i...
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| Main Authors: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2009
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2806603/ https://ncbi.nlm.nih.gov/pubmed/20084176 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1111/j.1467-9868.2009.00699.x |
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