惩罚支持向量机：一个用于特征选择支持向量机分类的R包。
penalizedSVM: a R-package for feature selection SVM classification.
作者信息
Becker Natalia, Werft Wiebke, Toedt Grischa, Lichter Peter, Benner Axel
机构信息
Division Molecular Genetics and Division Biostatistics, Heidelberg, Germany.
出版信息
Bioinformatics. 2009 Jul 1;25(13):1711-2. doi: 10.1093/bioinformatics/btp286. Epub 2009 Apr 27.
SUMMARY
Support vector machine (SVMs) classification is a widely used and one of the most powerful classification techniques. However, a major limitation is that SVM cannot perform automatic gene selection. To overcome this restriction, a number of penalized feature selection methods have been proposed. In the R package 'penalizedSVM' implemented penalization functions L(1) norm and Smoothly Clipped Absolute Deviation (SCAD) provide automatic feature selection for SVM classification tasks.
AVAILABILITY
The R package 'penalizedSVM' is available from the Comprehensive R Archive Network (http://cran.r-project.org/) under GPL-2 or later.
摘要
支持向量机（SVM）分类是一种广泛使用且最强大的分类技术之一。然而，一个主要限制是SVM无法执行自动基因选择。为克服这一限制，已提出了许多惩罚特征选择方法。在R包“penalizedSVM”中实现的惩罚函数L(1)范数和光滑截断绝对偏差（SCAD）为SVM分类任务提供自动特征选择。
可用性
R包“penalizedSVM”可从综合R存档网络（http://cran.r-project.org/）以GPL-2或更高版本获取。
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