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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Hindawi Publishing Corporation
2016
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5098106/ https://www.ncbi.nlm.nih.gov/pubmed/27843486 http://dx.doi.org/10.1155/2016/3456153 |