Bundle Methods for Regularized Risk Minimization Package


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Documentation for package ‘bmrm’ version 3.0

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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 (Vapnik et al. 1997)
fbetaLoss F beta score loss function
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
hingeLoss Hinge Loss function for SVM
ladRegressionLoss The loss function to perform a least absolute deviation regression
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 regularization and limited memory
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