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Efficient Regularized Regression with L (0) Penalty for Variable Selection and Network Construction
Variable selections for regression with high-dimensional big data have found many applications in bioinformatics and computational biology. One appealing approach is the L (0) regularized regression which penalizes the number of nonzero features in the model directly. However, it is well known that...
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| Pubblicato in: | Comput Math Methods Med |
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| Autori principali: | , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Hindawi Publishing Corporation
2016
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5098106/ https://ncbi.nlm.nih.gov/pubmed/27843486 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1155/2016/3456153 |
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