A community effort listing of reproducible research via open source software, open access to data and results, and open standards for interchange. [FREE]

Software for learning mixture distributions. Examples and two case studies are included. [Commercial]

A program compiler with built-in machine learning to find the most efficient compilation possible based upon the processor the program is run on. A collective optimization database, predictor web-service, and frameworks are available in order to make suggestions and further streamline the efficiency quotient of the user. This is the open source compiler that IBM has created press releases on using. [GPL]

Software toolkit for building and using motif-based hidden Markov models of DNA and proteins. There is an online interactive version. Source written in C. [GPL]

A software package to discover motifs (highly conserved regions) in groups of related DNA or protein sequences and, search sequence databases using motifs. [Commercial]

A library of tools for constructing maximum entropy (maxent) model in either python or C++. Some program features are L-BFGS and GIS parameter estimation, and gaussian prior smoothing. [GPL]

A Markov Logic Interpreter that focuses on efficient MAP inference and Online Learning featuring MAP inference using Cutting Planes combined with Max-Walk-Sat programming, parametrized weights, a shell interpreter, and cardinality constraints. [GPL]

An integrated collection of Java code useful for statistical natural language processing, document classification, clustering, information extraction, and other machine learning applications to text. [GPL]

Various classes, packages, macros and other software systems related to machine learning. [GPL]

Various software packages from the staff and alumni of The University of Texas at Austin which include inductions, a partial lazy evaluation, and a derivative of the Theo system. [GPL]

FTP repository of common list algorithms and datasets for research. [GPL]

Downloadable group of programs comprising the EMERALD machine learning system, (Experimental Machine Example-based Reasoning and Learning Disciple) integrates five modules (e.g. or 'robots'), each displaying a capability for machine learning, visualization, and interence. [LGPL]

A game programmed using evolutionary algorithm-based methodology allowing the game to learn by the player's decisions. There is also a method to use the alternative reinforcement learning decision tree to be used as well. Sourcecode is available for download. Python and Pygame are machine requirements. [GPL]

Index of software links for computer learning software. Scroll down. [FREE]

Made expressly for large-scale numerical and graphic applications, Lush can be used in programming scenarios where one needs to combine the flexibility of a high-level, weakly-typed interpreted language with the efficiency of a strongly-typed, native compiled language. Written in C and C++. [GPL]

A suite of Java libraries for the linguistic analysis of human language which can link entity mentions to database entries, uncover relations, cluster documents, and discover significant trends. [GPL]

A machine learning library. New implementations of various machine learning algorithms. [GPL]

A general purpose library for Bayesian filtering written in C++. [LGPL]

A software and library for huge classification implemented with the Adaptive Regularization of Weight Vectors (AROW) algorithm. [MIT License]

A library of classes for optimizing (training) the generic models. Written in C++. [GPL]

The aim of this project is to develop a Computational Environment for integrating the design and use of knowledge extraction models from data using evolutionary algorithms. Genetic learning may also be applied to the model. [GPL]

JProGraM is a machine learning library which supports learning and inference algorithms for Bayesian networks, Markov random fields, hybrid random fields, probabilistic decision trees, dependency networks, and Parzen windows. [GPL]

Many algorithms, toolboxes and full languages are available for scientific computing. Abstracts, PDF, and source code is available. [GPL]