About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the wellknown stochastic algorithms for Machine Learning developed in the highlevel technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and nonlinear Support Vector Machines applied to large data samples with usercentric and userfriendly emphasis. Changes:Initial Announcement on mloss.org.

About: Distributed optimization: Support Vector Machines and LASSO regression on distributed data Changes:Initial Upload

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: The Gesture Recognition Toolkit (GRT) is a crossplatform, opensource, c++ machine learning library that has been specifically designed for realtime gesture recognition. It features a large number of machinelearning algorithms for both classification and regression in addition to a wide range of supporting algorithms for preprocessing, feature extraction and dataset management. The GRT has been designed for realtime gesture recognition, but it can also be applied to more general machinelearning tasks. Changes:Added Decision Tree and Random Forests.

About: MLDemos is a userfriendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning. Changes:New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with nonnumerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bugfixes for display, import/export of data, classification performance New Algorithms and methodologies Added Projections to preprocess data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added GridSearch panel for batchtesting ranges of values for up to two parameters at a time Added OnevsAll multiclass classification for nonmulticlass algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)

About: The KernelMachine Library is a free (released under the LGPL) C++ library to promote the use of and progress of kernel machines. Changes:Updated mloss entry (minor fixes).

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: The library implements Optimized Cutting Plane Algorithm (OCAS) for efficient training of linear SVM classifiers from largescale data. Changes:Implemented COFFIN framework which allows efficient training of invariant image classifiers via virtual examples.

About: This software is designed for learning translation invariant kernels for classification with support vector machines. Changes:Initial Announcement on mloss.org.

About: LIBSVM is an integrated software for support vector classification, (CSVC, nuSVC ), regression (epsilonSVR, nuSVR) and distribution estimation (oneclass SVM). It supports multiclass [...] Changes:Initial Announcement on mloss.org.

About: SHARK is a modular C++ library for the design and optimization of adaptive systems. It provides various machine learning and computational intelligence techniques. Changes:

About: LibSGDQN proposes an implementation of SGDQN, a carefully designed quasiNewton stochastic gradient descent solver for linear SVMs. Changes:small bug fix (thx nicolas ;)

About: The Easysvm package provides a set of tools based on the Shogun toolbox allowing to train and test SVMs in a simple way. Changes:Fixes for shogun 0.7.3.

About: LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2regularized logistic regression (LR), L2loss linear SVM, L1loss linear SVM, and multiclass SVM Changes:Initial Announcement on mloss.org.

About: LaRank is an online solver for multiclass Support Vector Machines. Changes:Initial Announcement on mloss.org.

About: SVM Toolbox fully written in Matlab (even the QP solver). Features : SVM, MultiClassSVM, OneClass, SV Regression, AUCSVM and Rankboost, 1norm SVM, Regularization Networks, Kernel Basis Pursuit [...] Changes:Initial Announcement on mloss.org.

About: PyML is an interactive object oriented framework for machine learning in python with a focus on kernel methods. Changes:Initial Announcement on mloss.org.

About: BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes three methods Changes:Initial Announcement on mloss.org.

About: This is a C++ software designed to train largescale SVMs for binary classification. The algorithm is also implemented in parallel (**PGPDT**) for distributed memory, strictly coupled multiprocessor [...] Changes:Initial Announcement on mloss.org.
