Release 3.4 (11 Jul, 2017) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ * Replace bmrm() method by a new simplified implementation nrbmL1() * Change loss function interface: now return the given estimation point with attributes `lvalue` and `gradient` set. This change allow nrbm() to return typed values. * Adapt all loss functions to the new interface * Add predict() methods to all loss functions * Add machine learing helper functions `balanced.cv.fold()` and `roc.stat()` Release 3.2 (18 Apr, 2017) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ * Implement Linear Programmed version of SVM and multi-class-SVM with L1 regularization * Add ontologyLoss for class prediction problem that belong to an ontology structure * Add loss.weights argument to several loss functions to allow putting weights on each instance Release 3.0 (13 Jan, 2015) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ * Change loss function structure to now return the 1-arg function to optimize. This simplify overall code structure and make it more natural. For example, no more cache paramter is needed in the loss function. * Replace kernlab package by LowRankQP package to solve quadratic problems. This change fix a frequent bug in case of singular matrix * Replace clpAPI package by lpSolve package to solve linear programs. This new package is much easier to install * Implement NRBM algorithm of Do and Artieres (JMLR 2012) for non convex risk minimization Release 1.9 (2 Jun, 2014) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ * Fix an issue in costMatrix and ordinalRegressionLoss in case of missing labels * Replace internal S4 Solver object with a simpler environment Release 1.8 (10 Feb, 2014) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ * Change code structure to improve memory footprint. * Fix multiple issues with L2 regularization. * bmrm() now use L1 regularization by default. Release 1.7 (28 Jan, 2014) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ * The hingeLoss function (for SVM learning) gain a loss.weights paramter to handle unbalanced class distribution * Better handling of optimization parameter w0 in bmrm for hot starting the optimization process Release 1.6 (4 Sept, 2013) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ * Important fix in fbetaLoss: previous version was not correct and often lead to unsolvable optimization problem. * Improve memory footprint of L1 regularizer Release 1.5 (24 July, 2013) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ * Rename mlsRegressionLoss into lmsRegressionLoss * Implement new scalar losses: epsilonInsensitiveRegressionLoss, ladRegressionLoss, logisticRegression * Write a vignette for the package * Minor imrovment: Improve error messages * Minor imrovment: split the source code "scalarLoss.R" in 2 files "scalarClassificationLosses.R" and "scalarRegressionLosses.R" Release 1.4 (19 July, 2013) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ * Minor improvment: track the number of none zero element, and number of constraints in bmrm log. * Minor improvment: improve package description.