About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. Changes:20150709 Version 4.6 Development and release branches available at https://github.com/gpstuffdev/gpstuff New features
Improvements  faster EP moment calculation for lik_logit Several minor bugfixes

About: Python Machine Learning Toolkit Changes:Added LASSO (using coordinate descent optimization). Made SVM classification (learning and applying) much faster: 2.5x speedup on yeast UCI dataset.

About: A Tool for Embedding Strings in Vector Spaces Changes:Support for explicit selection of granularity added. Several minor bug fixes. We have reached 1.0

About: ModelBased Boosting Changes:Fetched by rcranrobot on 20130401 00:00:06.324985

About: A Laboratory for Recursive Partytioning Changes:Fetched by rcranrobot on 20130401 00:00:06.775432

About: CARP: The Clustering Algorithms’ Referee Package Changes:Generalized overlap error and some bugs have been fixed

About: The glmie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glmie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference. Changes:added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes generalised nonGaussian potentials so that affine instead of linear functions of the latent variables can be used

About: Python module to ease pattern classification analyses of large datasets. It provides highlevel abstraction of typical processing steps (e.g. data preparation, classification, feature selection, [...] Changes:
This release aggregates all the changes occurred between official
releases in 0.4 series and various snapshot releases (in 0.5 and 0.6
series). To get better overview of high level changes see
:ref:
Also adapts changes from 0.4.6 and 0.4.7 (see corresponding changelogs).
This is a special release, because it has never seen the general public.
A summary of fundamental changes introduced in this development version
can be seen in the :ref: Most notably, this version was to first to come with a comprehensive twoday workshop/tutorial.
A bugfix release
A bugfix release

About: FEAST provides implementations of common mutual information based filter feature selection algorithms (mim, mifs, mrmr, cmim, icap, jmi, disr, fcbf, etc), and an implementation of RELIEF. Written for C/C++ & Matlab. Changes:

About: MSVMpack is a Multiclass Support Vector Machine (MSVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four MSVM models from the literature: Weston and Watkins MSVM, Crammer and Singer MSVM, Lee, Lin and Wahba MSVM, and the MSVM2 of Guermeur and Monfrini. Changes:
