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Sparse generalized linear model with L(0) approximation for feature selection and prediction with big omics data
BACKGROUND: Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L (1), SCAD and MC+. However, none of the existing algorithms optimizes L (0), which penalizes the number of nonzero features directly. RE...
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| Publicado no: | BioData Min |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BioMed Central
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5735537/ https://ncbi.nlm.nih.gov/pubmed/29270229 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s13040-017-0159-z |
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