Bundle methods for minimization of convex and non-convex risk under L1 or L2 regularization. Implements the algorithm proposed by Teo et al. (JMLR 2010) as well as the extension proposed by Do and Artieres (JMLR 2012). The package comes with lot of loss functions for machine learning which make it powerful for big data analysis. The applications includes: structured prediction, linear SVM, multi-class SVM, f-beta optimization, ROC optimization, ordinal regression, quantile regression, epsilon insensitive regression, least mean square, logistic regression, least absolute deviation regression (see package examples), etc... all with L1 and L2 regularization.
| Version: | 4.1 |
| Depends: | R (≥ 3.0.2) |
| Imports: | methods, lpSolve, LowRankQP, matrixStats, Rcpp |
| LinkingTo: | Rcpp |
| Suggests: | knitr |
| Published: | 2019-04-03 |
| Author: | Julien Prados |
| Maintainer: | Julien Prados <julien.prados at unige.ch> |
| License: | GPL-3 |
| Copyright: | 2017, University of Geneva |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| In views: | MachineLearning |
| CRAN checks: | bmrm results |
| Reference manual: | bmrm.pdf |
| Vignettes: |
bmrm User Guide |
| Package source: | bmrm_4.1.tar.gz |
| Windows binaries: | r-devel: bmrm_4.1.zip, r-release: bmrm_4.1.zip, r-oldrel: bmrm_4.1.zip |
| macOS binaries: | r-release: bmrm_4.1.tgz, r-oldrel: bmrm_4.1.tgz |
| Old sources: | bmrm archive |
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