bmrm: Bundle Methods for Regularized Risk Minimization Package

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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.

Author
Julien Prados
Date of publication
2015-01-15 16:59:17
Maintainer
Julien Prados <julien.prados@unige.ch>
License
GPL-3
Version
3.0

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Man pages

bmrm
Bundle Methods for Regularized Risk Minimization
costMatrix
Compute or check the structure of a cost matrix
epsilonInsensitiveRegressionLoss
The loss function to perform a epsilon-insensitive regression...
fbetaLoss
F beta score loss function
gradient
Return or set gradient attribute
hingeLoss
Hinge Loss function for SVM
ladRegressionLoss
The loss function to perform a least absolute deviation...
lmsRegressionLoss
The loss function to perform a least mean square regression
logisticRegressionLoss
The loss function to perform a logistic regression
nrbm
Convex and non-convex risk minimization with L2...
ordinalRegressionLoss
The loss function for ordinal regression
quantileRegressionLoss
The loss function to perform a quantile regression
rocLoss
The loss function to maximize area under the ROC curve
softMarginVectorLoss
Soft Margin Vector Loss function for multiclass SVM

Files in this package

bmrm
bmrm/inst
bmrm/inst/doc
bmrm/inst/doc/bmrm.R
bmrm/inst/doc/bmrm.Rnw
bmrm/inst/doc/bmrm.pdf
bmrm/NAMESPACE
bmrm/NEWS
bmrm/R
bmrm/R/cost.R
bmrm/R/bmrm.R
bmrm/R/scalarClassificationLosses.R
bmrm/R/nrbm.R
bmrm/R/vectorialLoss.R
bmrm/R/loss.R
bmrm/R/scalarRegressionLosses.R
bmrm/vignettes
bmrm/vignettes/bmrm.bib
bmrm/vignettes/bmrm.Rnw
bmrm/MD5
bmrm/build
bmrm/build/vignette.rds
bmrm/DESCRIPTION
bmrm/man
bmrm/man/gradient.Rd
bmrm/man/bmrm.Rd
bmrm/man/epsilonInsensitiveRegressionLoss.Rd
bmrm/man/ordinalRegressionLoss.Rd
bmrm/man/lmsRegressionLoss.Rd
bmrm/man/logisticRegressionLoss.Rd
bmrm/man/nrbm.Rd
bmrm/man/quantileRegressionLoss.Rd
bmrm/man/rocLoss.Rd
bmrm/man/hingeLoss.Rd
bmrm/man/costMatrix.Rd
bmrm/man/fbetaLoss.Rd
bmrm/man/ladRegressionLoss.Rd
bmrm/man/softMarginVectorLoss.Rd