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 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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Last month downloads


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

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