About: The CAM R-Java software provides a noval way to solve blind source separation problem.

1. Three classic BSS algorithms - NMF, fastICA and Factor Analysis - are combined into the software. Users can readily call the three functions from Java GUI
2. A simple plug-in mechanism is added. Users can add their own BSS algorithm into the software by following the Software Plugin Adding Guide and User Manual

About: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semi-supervised tasks.

Fixed bug in IndexedFile, which caused esvm-train to fail when used without bootstrap mask. Library API/ABI remain unchanged, library revision increased.

About: Probabilistic performance evaluation for multiclass classification using the posterior balanced accuracy

Readme added. Explanation of the examples in the readme file.