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.
Release 3.0 (13 Jan, 2015)
* Change loss function structure to now return the 1-arg function to optimize.This simplify overall code structure and make it more natural. For example,no more cache paramter is needed in the loss function.* Replace kernlab package by LowRankQP package to solve quadratic problems. Thischange fix a frequent bug in case of singular matrix* Replace clpAPI package by lpSolve package to solve linear programs. This newpackage is much easier to install* Implement NRBM algorithm of Do and Artieres (JMLR 2012) for non convex riskminimizationRelease 1.9 (2 Jun, 2014)
Release 1.8 (10 Feb, 2014)
* Change code structure to improve memory footprint.* Fix multiple issues with L2 regularization.* bmrm() now use L1 regularization by default.Release 1.7 (28 Jan, 2014)
Release 1.6 (4 Sept, 2013)
* Important fix in fbetaLoss: previous version was not correct and often lead to unsolvable optimization problem.* Improve memory footprint of L1 regularizerRelease 1.5 (24 July, 2013)
Release 1.4 (19 July, 2013)
* Minor improvment: track the number of none zero element, and number of constraints in bmrm log.* Minor improvment: improve package description.