penalizedSVM

R package penalizedSVM: Feature Selection SVM using penalty functions. This package provides feature selection SVM using penalty functions. The smoothly clipped absolute deviation (SCAD), ’L1-norm’, ’Elastic Net’ (’L1-norm’ and ’L2-norm’) and ’Elastic SCAD’ (SCAD and ’L2-norm’) penalties are availible. The tuning parameters can be founf using either a fixed grid or a interval search.

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References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
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  1. Pan, Yuqing; Mai, Qing; Zhang, Xin: Covariate-adjusted tensor classification in high dimensions (2019)
  2. Relión, Jesús D. Arroyo; Kessler, Daniel; Levina, Elizaveta; Taylor, Stephan F.: Network classification with applications to brain connectomics (2019)
  3. Yuqing Pan, Qing Mai, Xin Zhang: TULIP: A Toolbox for Linear Discriminant Analysis with Penalties (2019) arXiv
  4. Wang, Chamont; Gevertz, Jana L.: Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches (2016)
  5. Martin Sill; Thomas Hielscher; Natalia Becker; Manuela Zucknick: c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models (2014) not zbMATH
  6. Reif, Matthias; Shafait, Faisal: Efficient feature size reduction via predictive forward selection (2014) ioport
  7. Collignon, O.; Monnez, J.-M.; Vallois, P.; Codreanu, F.; Renaudin, J.-M.; Kanny, G.; Brulliard, M.; Bihain, B. E.; Jacquenet, S.; Moneret-Vautrin, D.: Discriminant analyses of peanut allergy severity scores (2011)
  8. Wang, Yuanjia; Chen, Huaihou; Li, Runze; Duan, Naihua; Lewis-Fernández, Roberto: Prediction-based structured variable selection through the receiver operating characteristic curves (2011)
  9. Becker, Natalia; Werft, Wiebke; Toedt, Grischa; Lichter, Peter; Benner, Axel: Penalizedsvm: a R-package for feature selection SVM classification (2009) ioport