Bundle methods for minimization of convex and nonconvex 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, multiclass SVM, fbeta 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.
Package details 


Author  Julien Prados 
Date of publication  20170519 21:24:23 UTC 
Maintainer  Julien Prados <julien.prados@unige.ch> 
License  GPL3 
Version  3.3 
Package repository  View on CRAN 
Installation 
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