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.


