In this paper, we develop an efficient method for sparse support vector machines with L 0 norm approximation.
Sparse support vector machines with L0 approximation for ultra-high ...
pubmed.ncbi.nlm.nih.gov › ...
Omics data usually have ultra-high dimension (p) and small sample size (n). Standard support vector machines (SVMs), which minimize the L2 norm for the primal ...
Mar 30, 2019 · When applied to high-dimensional big omics data, biologically significant taxa and genes with strong associations can be identified. The ...
Dive into the research topics of 'Sparse support vector machines with L0 approximation for ultra-high dimensional omics data'. Together they form a unique ...
May 1, 2019 · L 0 SVM outperformed L 1 SVM in that it identifies true features more accurately with less false positive rate. •. Implemented with dual ...
Data for: Sparse Support Vector Machines with L0 Approximation for Ultra-high Dimensional Omics Data. Published: 7 May 2019| Version 1 | DOI: ...
Sparse support vector machines with L0 approximation for ultra-high dimensional omics data ... The backbone method for ultra-high dimensional sparse machine ...
Bibliographic details on Sparse support vector machines with L 0 approximation for ultra-high dimensional omics data.
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In this paper, we propose a novel method for sparse support vector machines (SVMs) with L_{p} (p < 1) regularization. Efficient algorithms (LpSVM) are developed ...
Learning Sparse SVM for Feature Selection on Very High ...
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In this paper, by introducing a 0-1 control variable to each input feature, l0-norm Sparse SVM (SSVM) is converted to a mixed integer programming (MIP) problem.