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About: MOSIS is a modularized framework for signal processing, stream analysis, machine learning and stream mining applications. Changes:

About: HierLearning is a C++11 implementation of a generalpurpose, multiagent, hierarchical reinforcement learning system for sequential decision problems. Changes:Initial Announcement on mloss.org.

About: DAL is an efficient and flexibible MATLAB toolbox for sparse/lowrank learning/reconstruction based on the dual augmented Lagrangian method. Changes:

About: The package "fastclime" provides a method of recover the precision matrix efficiently by applying parametric simplex method. The computation is based on a linear optimization solver. It also contains a generic LP solver and a parameterized LP solver using parametric simplex method. Changes:Initial Announcement on mloss.org.

About: BudgetedSVM is an opensource C++ toolbox for scalable nonlinear classification. The toolbox can be seen as a missing link between LibLinear and LibSVM, combining the efficiency of linear with the accuracy of kernel SVM. We provide an Application Programming Interface for efficient training and testing of nonlinear classifiers, supported by data structures designed for handling data which cannot fit in memory. We also provide commandline and Matlab interfaces, providing users with an efficient, easytouse tool for largescale nonlinear classification. Changes:Changed license from LGPL v3 to Modified BSD.

About: The package computes the optimal parameters for the Choquet kernel Changes:Initial Announcement on mloss.org.

About: Estimates statistical significance of association between variables and their principal components (PCs). Changes:Initial Announcement on mloss.org.

About: "Ordinal Choquistic Regression" model using the maximum likelihood Changes:Initial Announcement on mloss.org.

About: We introduces PLL, a parallel LDA learning toolbox for big topic modeling. Changes:Fix some compiling errors.

About: DRVQ is a C++ library implementation of dimensionalityrecursive vector quantization, a fast vector quantization method in highdimensional Euclidean spaces under arbitrary data distributions. It is an approximation of kmeans that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a byproduct of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast. Changes:Initial Announcement on mloss.org.
