title: penalizedSVM: a R-package for feature selection SVM classification.
abstract: CONCLUSIONS
  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.
  
  
  BACKGROUND
  The R package 'penalizedSVM' is available from the Comprehensive R Archive Network (http://cran.r-project.org/) under GPL-2 or later.
authors.affiliation: Division Molecular Genetics and Division Biostatistics, Heidelberg, Germany. natalia.becker@dkfz.de
authors.forename: Natalia
authors.initials: N
authors.lastname: Becker
authors.forename: Wiebke
authors.initials: W
authors.lastname: Werft
authors.forename: Grischa
authors.initials: G
authors.lastname: Toedt
authors.forename: Peter
authors.initials: P
authors.lastname: Lichter
authors.forename: Axel
authors.initials: A
authors.lastname: Benner
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D012984:Software