# bmrm v4.1

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## Bundle Methods for Regularized Risk Minimization Package

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.

## Functions in bmrm

Name | Description | |

is.convex | Return or set is.convex attribute | |

balanced.cv.fold | Split a dataset for Cross Validation taking into account class balance | |

bhattacharyya.coefficient | Compute Bhattacharyya coefficient needed for Hellinger distance | |

linearRegressionLoss | Loss functions to perform a regression | |

hellinger.dist | Compute Hellinger distance | |

hclust_fca | Find first common ancestor of 2 nodes in an hclust object | |

balanced.loss.weights | Compute loss.weights so that total losses of each class is balanced | |

ordinalRegressionLoss | The loss function for ordinal regression | |

predict.mmc | Predict class of new instances according to a mmc model | |

rank.linear.weights | Rank linear weight of a linear model | |

iterative.hclust | Perform multiple hierachical clustering on random subsets of a dataset | |

roc.stat | Compute statistics for ROC curve plotting | |

mmcLoss | Loss function for max-margin clustering | |

preferenceLoss | The loss function for Preference loss | |

lpSVM | Linearly Programmed SVM | |

print.roc.stat | Generic method overlad to print object of class roc.stat | |

multivariateHingeLoss | The loss function for multivariate hinge loss | |

softmaxLoss | softmax Loss Function | |

binaryClassificationLoss | Loss functions for binary classification | |

wolfe.linesearch | Wolfe Line Search | |

lvalue | Return or set lvalue attribute | |

mmc | Convenient wrapper function to solve max-margin clustering problem on a dataset | |

rowmean | Columun means of a matrix based on a grouping variable | |

softMarginVectorLoss | Soft Margin Vector Loss function for multiclass SVM | |

costMatrix | Compute or check the structure of a cost matrix | |

gradient | Return or set gradient attribute | |

nrbm | Convex and non-convex risk minimization with L2 regularization and limited memory | |

ontologyLoss | Ontology Loss Function | |

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## Vignettes of bmrm

Name | ||

bmrm.Rmd | ||

bmrm.bib | ||

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## Last month downloads

## Details

Type | Package |

Date | 2019-04-03 |

VignetteBuilder | knitr |

Copyright | 2017, University of Geneva |

License | GPL-3 |

RoxygenNote | 6.1.0 |

LinkingTo | Rcpp |

NeedsCompilation | yes |

Packaged | 2019-04-03 13:45:52 UTC; root |

Repository | CRAN |

Date/Publication | 2019-04-03 14:00:03 UTC |

suggests | knitr |

imports | LowRankQP , lpSolve , matrixStats , methods , Rcpp |

depends | R (>= 3.0.2) |

Contributors | Julien Prados |

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