About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more. Changes:Python 2 & 3 interface fixes and minor updates. Now uses TravisCI as well.

About: JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, NadarayaWatson estimator); (3) generative models for random networks (smallworld, scalefree, exponential random graphs, Fiedler random graphs/fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition. Changes:JProGraM 13.2  CHANGE LOG Release date: February 13, 2012 New features:  Support for Fiedler random graphs/random field models for largescale networks (ninofreno.graph.fiedler package);  Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).

About: The gmm toolbox contains code for density estimation using mixtures of Gaussians: Starting from simple kernel density estimation with spherical and diagonal Gaussian kernels over manifold Parzen window until mixtures of penalised full Gaussians with only a few components. The toolbox covers many Gaussian mixture model parametrisations from the recent literature. Most prominently, the package contains code to use the Gaussian Process Latent Variable Model for density estimation. Most of the code is written in Matlab 7.x including some MEX files. Changes:Initial Announcement on mloss.org

About: PLearn is a large C++ machinelearning library with a set of Python tools and Python bindings. It is mostly a research platform for developing novel algorithms, and is being used extensively at [...] Changes:Initial Announcement on mloss.org.
