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Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification
We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regress...
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| Yayımlandı: | J Am Stat Assoc |
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| Asıl Yazarlar: | , , , |
| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
2016
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| Konular: | |
| Online Erişim: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4866821/ https://ncbi.nlm.nih.gov/pubmed/27185970 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/01621459.2015.1005212 |
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