About: machine learning library in java for easy development of new kernels and kernel algorithms Changes:Version 3.0 /! Warning: this version is incompatible with previous code

About: Kernelbased Learning Platform (KeLP) is Java framework that supports the implementation of kernelbased learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernelmachine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vectorbased to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate prediction models without writing a single line of code. Changes:In addition to minor bug fixes, this release includes:
Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.0.2!

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 has focused on build system improvements, both for the Python API and C++ builds using CMake. This includes adding a setup.py script for installing the dlib Python API as well as a make install target for installing a C++ shared library for nonPython use.

About: The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line. Changes:This release features the work of our 8 GSoC 2014 students [student; mentors]:
It also contains several cleanups and bugfixes: Features
Bugfixes
Cleanup and API Changes

About: C++ generic programming tools for machine learning Changes:Initial Announcement on mloss.org.

About: The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building productiongrade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details. Changes:Adding a large number of new distributions, such as AndersonDaring, ShapiroWilk, Inverse ChiSquare, Lévy, Folded Normal, Shifted LogLogistic, Kumaraswamy, Trapezoidal, Uquadratic and BetaPrime distributions, BirnbaumSaunders, Generalized Normal, Gumbel, Power Lognormal, Power Normal, Triangular, Tukey Lambda, Logistic, Hyperbolic Secant, Degenerate and General Continuous distributions. Other additions include new statistical hypothesis tests such as AndersonDaring and ShapiroWilk; as well as support for all of LIBLINEAR's support vector machine algorithms; and format reading support for MATLAB/Octave matrices, LibSVM models, sparse LibSVM data files, and many others. For a complete list of changes, please see the full release notes at the release details page at: https://github.com/accordnet/framework/releases

About: This is a library for solving nuSVM by using Wolfe's minimum norm point algorithm. You can solve binary classification problem. Changes:Initial Announcement on mloss.org.

About: A MATLAB toolbox for defining complex machine learning comparisons Changes:Initial Announcement on mloss.org.

About: A MATLAB toolkit for performing generalized regression with equality/inequality constraints on the function value/gradient. Changes:Initial Announcement on mloss.org.

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:

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: Support Vectors Machine library in .net with CUDA support. Library includes GPU SVM solver for kernels linear,RBF,ChiSquare and Exp ChiSquare which use NVIDIA CUDA technology. It allows for classification of feature rich sparse datasets through utilization of sparse matrix formats CSR, EllpackR or Sliced EllRT Changes:Initial Announcement on mloss.org.

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: The UniverSVM is a SVM implementation written in C/C++. Its functionality comprises large scale transduction via CCCP optimization, sparse solutions via CCCP optimization and datadependent [...] Changes:Minor changes: fix bug on set_alphas_b0 function (thanks to Ferdinand Kaiser  ferdinand.kaiser@tut.fi)

About: A general purpose library to process and predict sequences of elements using echo state networks. Changes:Initial Announcement on mloss.org.

About: "Pattern" is a web mining module for Python. It bundles tools for data retrieval, text analysis, clustering and classification, and data visualization. Changes:

About: Matlab code for learning probabilistic SVM in the presence of uncertain labels. Changes:Added missing dataset function (thanks to Hao Wu)

About: mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL. Changes:New features:
Fix:
