About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for nonGaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISSGP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models. Changes:A major code restructuring effort did take place in the current release unifying certain inference functions and allowing more flexibility in covariance function composition. We also redesigned the whole derivative computation pipeline to strongly improve the overall runtime. We finally include gridbased covariance approximations natively. More generic sparse approximation using Power EP
Approximate covariance object unifying sparse approximations, gridbased approximations and exact covariance computations
Hiearchical structure of covariance functions
Faster derivative computations for mean and cov functions
New mean functions
New optimizer
New GLM link function
Smaller fixes

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls. Changes:

About: A Java Toolbox for Scalable Probabilistic Machine Learning. Changes:
Detailed information can be found in the toolbox's web page

About: A library of scalable Bayesian generalised linear models with fancy features Changes:

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. Changes:This release adds a number of new features, most important of which is a deep convolutional neural network version of the maxmargin object detection algorithm. This tool makes it very easy to create high quality object detectors. See http://dlib.net/dnn_mmod_ex.cpp.html for an introduction.

About: Somoclu is a massively parallel implementation of selforganizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, Julia, R, and MATLAB are supported. Changes:

About: Cox models by likelihood based boosting for a single survival endpoint or competing risks Changes:Fetched by rcranrobot on 20161001 00:00:04.178988

About: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly Changes:Fetched by rcranrobot on 20161001 00:00:04.307859

About: Wrapper Algorithm for All Relevant Feature Selection Changes:Fetched by rcranrobot on 20161001 00:00:03.742650

About: RLScore  regularized leastsquares machine learning algorithms package Changes:Initial Announcement on mloss.org.
