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Sparse Support Vector Machines with L(0) Approximation for Ultra-high Dimensional Omics Data
Omics data usually have ultra-high dimension (p) and small sample size (n). Standard support vector machines (SVMs), which minimize the L(2) norm for the primal variables, only lead to sparse solutions for the dual variables. L(1) based SVMs, directly minimizing the L(1) norm, have been used for fea...
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| Yayımlandı: | Artif Intell Med |
|---|---|
| Asıl Yazarlar: | , , |
| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
2019
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| Konular: | |
| Online Erişim: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6553498/ https://ncbi.nlm.nih.gov/pubmed/31164207 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.artmed.2019.04.004 |
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