MLPACK 2.0.0 发布，主要更新如下：
* Parallelization: the DET (density estimation trees) code is now parallelized with OpenMP. As time goes on, parallelization will be added to other algorithms, but note that you can also use Armadillo with OpenBLAS, which will parallelize all the linear algebra calls. * Model saving and loading: where appropriate, all of the command-line programs now support loading and saving models. So you can train, say, a logistic regression model, and save it for later use. This is also possible with techniques like all-k-nearest-neighbor search, which allow you to save the tree built on the points. Model serialization support is also available from C++, too, of course. * Significant refactoring: most machine learning algorithms now follow the same API, and documentation has been improved. * Tree-based algorithms now support multiple types of trees in a far easier manner. * The k-means code now supports five different algorithms, many of them far faster than the original implementation. * Add streaming decision trees (Hoeffding trees) for fast classifiers on huge datasets. This supports both categorical and numeric features. * No more dependence on libxml2; boost::serialization is used instead. * Armadillo minimum version bump to 4.100.0. * All mlpack programs are now prefixed with 'mlpack_', so for instance 'allknn' is now 'mlpack_allknn'.
MLPACK 是一个 C++ 的机器学习库，其重点是可伸缩性、速度和易用。