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Penalized Bregman divergence for large-dimensional regression and classification
Regularization methods are characterized by loss functions measuring data fits and penalty terms constraining model parameters. The commonly used quadratic loss is not suitable for classification with binary responses, whereas the loglikelihood function is not readily applicable to models where the...
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| Päätekijät: | , , |
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| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
Oxford University Press
2010
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3372245/ https://ncbi.nlm.nih.gov/pubmed/22822248 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/biomet/asq033 |
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