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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...
שמור ב:
| הוצא לאור ב: | Artif Intell Med |
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| Main Authors: | , , |
| פורמט: | Artigo |
| שפה: | Inglês |
| יצא לאור: |
2019
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| נושאים: | |
| גישה מקוונת: | 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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