About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance.

Initial Announcement on mloss.org.

About: The scikit-learn aims to provide state of the art standard machine learning algorithms in Python.

Initial Announcement on mloss.org.

About: Universal Python-written numerical optimization toolbox. Problems: NLP, LP, QP, NSP, MILP, LSP, LLSP, MMP, GLP, SLE etc; automatic differentiation is available

http://openopt.org/Changelog

About: A C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.

This release adds a general purpose implementation of the OCA optimizer, OCAS SVM trainer, and support for loading and saving LIBSVM formatted data files.

About: The CTBN-RLE is a C++ package of executables and libraries for inference and learning algorithms for continuous time Bayesian networks (CTBNs).

Minor code changes (a few compilation issues, #define .h guard name changes).

About: A Java framework for statistical analysis and classification of biological sequences

March 2, 2010: Jstacs 1.3.1 released

• Partitioning of Samples including weights

• Release of Dispom (de-novo discovery of differentially abundant transcription factor binding sites including their positional preference)

• Several bugfixes

About: libDAI provides FOSS implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

New features include:

* approximate inference method "Fractional Belief Propagation" (contributed by Frederik Eaton)
* approximate inference method "Tree-Reweighted Belief Propagation"

Apart from that, this release various code cleanups, bug fixes, added examples, and documentation improvements.

About: Armadillo is a template C++ linear algebra library aiming towards a good balance between speed and ease of use. Matrix decompositions are provided through optional integration with LAPACK and ATLAS.

• extended and overhauled expression evaluation framework, for faster handling of compound expressions
• improvements in the documentation, including a conversion table between Matlab and Armadillo syntax

About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

Initial Announcement on mloss.org.

About: A Java library to create, process and manage mixtures of exponential families.

Initial Announcement on mloss.org.

About: Machine Learning PYthon (mlpy) is a high-performance Python package for predictive modeling.

New features:

• Svm optimal offset option added
• FSSun for feature weighting/selection added
• Dlda: adaptive offset for classification implemented
• Srda: memory usage optimization, speeded up
• added Tversky kernel for SVM

Bug fixes:

• fixed gaussian weights for SVM

About: MLPACK is the first comprehensive scalable machine learning library.

Initial Announcement on mloss.org.

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.

This release contains several enhancements, cleanups and bugfixes:

Features

• Integrate LaRank.
• Memory Mapped Features (for data sets that don't fit into memory).
• Compressor module with compression and decompression support for lzo, gzip, bzip2 and lzma.
• Compressed String Features with on-the-fly decompression (CDecompressString preproc).
• Parallel computation of get_kernel_matrix().
• One may now prefix all shogun print/outputs with file name and line number (obj.io.enable_file_and_line())
• Chinese Documentation thanks Elpmis Lee.

Bugfixes

• Fix One class MKL testing in static interfaces.
• Configure fixes: Let octave not write history on configure; fail when cplex is forcefully enabled but not found; add cplex 12 support.
• Fix a problem with regression and CombinedKernels employing only Custom kernels.

Cleanup and API Changes

• String Features now (like SimpleFeatures) upon get_feature_vector require an additional do_free argument and need to be freed using free_feature_vector.

About: Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its [...]

Version 1.2.3

• Corrected bugs in the Python module (missing Py_DECREF in functions returning multiple numpy arrays that led to a memory leak, as well as bad preprocessor directive "USE_EXPAT" instead of "HAVE_LIBEXPAT") Thanks to Benjamin Dittes

• Corrected bad preprocessor directive (see above) in C++ wrapper lwpr.hh. Also added casts between signed and unsigned integers in lwpr.hh and cross.cc Thanks to Peter Pastor, Robert Nickl and Adrian Haith

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

• new build system
• minor bug fixes

About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...]

This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer.

Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic).

Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss

Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions.

Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures).

Unified automatic input checking via new static typing extending Python properties.

Full support for recursive composition of larger components containing arbitrary statically typed state variables.

About: BioSig is a software library for biomedical signal processings. Besides several other modules, one modul (t400) provides a common interface (train_sc.m and test_sc.m) to various classification [...]

Update of project information: machine learning and classification tools are moved to the NaN-toolbox.

About: The Computational Infrastructure for Operations Research (COIN-OR) project is an initiative to spur the development of open-source software for the operations research community.

Initial Announcement on mloss.org.

About: SeDuMi is a software package to solve optimization problems over symmetric cones. This includes linear, quadratic, second order conic and semidefinite optimization, and any combination of these.

Initial Announcement on mloss.org.