About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications. Changes:

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: Orange is a componentbased machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...] Changes:The core of the system (except the GUI) no longer includes any GPL code and can be licensed under the terms of BSD upon request. The graphical part remains under GPL. Changed the BibTeX reference to the paper recently published in JMLR MLOSS.

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: Mulan is an opensource Java library for learning from multilabel datasets. Multilabel datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multilabel dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions. Changes:Learners
Measures/Evaluation
Bug fixes
API changes
Miscellaneous

About: MLwizard recommends and optimizes classification algorithms based on metalearning and is a software wizard fully integrated into RapidMiner but can be used as library as well. Changes:Faster parameter optimization using genetic algorithm with predefined start population.

About: Motivated by a need to classify highdimensional, heterogeneous data from the bioinformatics domain, we developed MLFlex, a machinelearning toolbox that enables users to perform twoclass and multiclass classiﬁcation analyses in a systematic yet ﬂexible manner. MLFlex was written in Java but is capable of interfacing with thirdparty packages written in other programming languages. It can handle multiple inputdata formats and supports a variety of customizations. MLFlex provides implementations of various validation strategies, which can be executed in parallel across multiple computing cores, processors, and nodes. Additionally, MLFlex supports aggregating evidence across multiple algorithms and data sets via ensemble learning. (See http://jmlr.csail.mit.edu/papers/volume13/piccolo12a/piccolo12a.pdf.) Changes:Initial Announcement on mloss.org.

About: This package is a set of Matlab scripts that implements the algorithms described in the submitted paper: "LpLq Sparse Linear and Sparse Multiple Kernel MultiTask Learning". Changes:Initial Announcement on mloss.org.

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

About: Matlab SVM toolbox for learning large margin filters in signal or images. Changes:Initial Announcement on mloss.org.

About: NaNtoolbox is a statistics and machine learning toolbox for handling data with and without missing values. Changes:Changes in v.2.5.2  faster version of quantile if multiple quantiles are requested  removes the dependency on ZLIB and thus  fixes "pkg install nan" for Octave on Windows  a number of minor improvements For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG

About: MATLAB toolbox for advanced BrainComputer Interface (BCI) research. Changes:Initial Announcement on mloss.org.

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 original Random Forests implementation by Breiman and Cutler. Changes:Initial Announcement on mloss.org.

About: C5.0 is the successor of the C4.5 decision tree algorithm/tool. In particular, it is faster and more memoryefficient. Changes:Initial Announcement on mloss.org.

About: Matlab implementation of variational gaussian approximate inference for Bayesian Generalized Linear Models. Changes:Code restructure and bug fix.

About: Pyriel is a Python system for learning classification rules from data. Unlike other rule learning systems, it is designed to learn rule lists that maximize the area under the ROC curve (AUC) instead of accuracy. Pyriel is mostly an experimental research tool, but it's robust and fast enough to be used for lightweight industrial data mining. Changes:1.5 Changed CF (confidence factor) to do LaPlace smoothing of estimates. New flag "scoreforclass C" causes scores to be computed relative to a given (positive) class. For twoclass problems. Fixed bug in example sampling code (sample n) Fixed bug keeping oldstyle example formats (terminated by dot) from working. More code restructuring.

About: KeplerWeka represents the integration of all the functionality of the WEKA Machine Learning Workbench into the opensource scientific workflow Kepler. Among them are classification, [...] Changes:
