Bundle Methods for Regularized Risk Minimization Package


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Documentation for package ‘bmrm’ version 4.1

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balanced.cv.fold Split a dataset for Cross Validation taking into account class balance
balanced.loss.weights Compute loss.weights so that total losses of each class is balanced
bhattacharyya.coefficient Compute Bhattacharyya coefficient needed for Hellinger distance
binaryClassificationLoss Loss functions for binary classification
costMatrix Compute or check the structure of a cost matrix
epsilonInsensitiveRegressionLoss Loss functions to perform a regression
fbetaLoss Loss functions for binary classification
gradient Return or set gradient attribute
gradient.default Return or set gradient attribute
gradient<- Return or set gradient attribute
gradient<-.default Return or set gradient attribute
hclust_fca Find first common ancestor of 2 nodes in an hclust object
hellinger.dist Compute Hellinger distance
hingeLoss Loss functions for binary classification
is.convex Return or set is.convex attribute
is.convex.default Return or set is.convex attribute
is.convex<- Return or set is.convex attribute
is.convex<-.default Return or set is.convex attribute
iterative.hclust Perform multiple hierachical clustering on random subsets of a dataset
ladRegressionLoss Loss functions to perform a regression
linearRegressionLoss Loss functions to perform a regression
lmsRegressionLoss Loss functions to perform a regression
logisticLoss Loss functions for binary classification
lpSVM Linearly Programmed SVM
lvalue Return or set lvalue attribute
lvalue.default Return or set lvalue attribute
lvalue<- Return or set lvalue attribute
lvalue<-.default Return or set lvalue attribute
mmc Convenient wrapper function to solve max-margin clustering problem on a dataset
mmcLoss Loss function for max-margin clustering
multivariateHingeLoss The loss function for multivariate hinge loss
nrbm Convex and non-convex risk minimization with L2 regularization and limited memory
nrbmL1 Convex and non-convex risk minimization with L2 regularization and limited memory
ontologyLoss Ontology Loss Function
ordinalRegressionLoss The loss function for ordinal regression
predict.mmc Predict class of new instances according to a mmc model
predict.svmLP Linearly Programmed SVM
predict.svmMLP Linearly Programmed SVM
preferenceLoss The loss function for Preference loss
print.roc.stat Generic method overlad to print object of class roc.stat
quantileRegressionLoss Loss functions to perform a regression
rank.linear.weights Rank linear weight of a linear model
roc.stat Compute statistics for ROC curve plotting
rocLoss Loss functions for binary classification
rowmean Columun means of a matrix based on a grouping variable
softMarginVectorLoss Soft Margin Vector Loss function for multiclass SVM
softmaxLoss softmax Loss Function
svmLP Linearly Programmed SVM
svmMulticlassLP Linearly Programmed SVM
wolfe.linesearch Wolfe Line Search