Bundle Methods for Regularized Risk Minimization Package

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

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

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

binaryClassificationLoss | Loss functions for binary classification |

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

gradient | Return or set gradient attribute |

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

hellinger.dist | Compute Hellinger distance |

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

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

linearRegressionLoss | Loss functions to perform a regression |

lpSVM | Linearly Programmed SVM |

lvalue | Return or set lvalue attribute |

mmc | Convenient wrapper function to solve max-margin clustering... |

mmcLoss | Loss function for max-margin clustering |

multivariateHingeLoss | The loss function for multivariate hinge loss |

nrbm | Convex and non-convex risk minimization with L2... |

ontologyLoss | Ontology Loss Function |

ordinalRegressionLoss | The loss function for ordinal regression |

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

preferenceLoss | The loss function for Preference loss |

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

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

roc.stat | Compute statistics for ROC curve plotting |

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

softMarginVectorLoss | Soft Margin Vector Loss function for multiclass SVM |

softmaxLoss | softmax Loss Function |

wolfe.linesearch | Wolfe Line Search |

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