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OpenSkyNet - Moving towards a comprehensive artificial intelligence solution for game developers under the LGPL. The goals are to implement action selection solvers, robust steering behaviors (including pathfinding algorithms), and machinelearning.
The intention of this project is to give all serious users of the SNNS a place where they find a bugfix and patch management and where they get useful information about the SNNS.
This project is devoted to the creation of an open source Error-Correcting Output Codes (ECOC) library for the MachineLearning community. The ECOC framework is a powerful tool to deal with multi-class categorization problems.
DOGMA is a MATLAB toolbox for discriminative online learning. It implements all the state of the art algorithms in a unique and simple framework. Examples are Perceptron, Passive-Aggresive, ALMA, NORMA, SILK, Projectron, RBP, Banditron, etc.
This RapidMiner-plugin consists of operators for feature selection and classification - mainly on high-dimensional (microarray-) data - and some helper-classes/operators.
MoMS (Model Management System) is a model management system for statistical models, a little bit like a database management system. Instead of having tables, we have models that can be updated and queried.
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The Mars Rover Simulator project is based on the evolutionary robotics paradigm where an artificial agent acquires its skills through the process of artificial evolution. This simulator can be useful to evolve neural network controllers for the rover
Cougar Squared is a new Java library for machinelearning and data mining research, supporting research needs of the community. It is written by researchers for researchers. It extends the WEKA and YALE machinelearning frameworks.
The Python Computer Vision Framework is an opened project deisgned for all those interested in computer vision. It aims at making computer vision more easy and structured and matlab-free.
It may also be used for other artistic and scientific areas.
Feating constructs a classification ensemble comprising a set of local models. It is effective at reducing the error of both stable and unstable learners, including SVM. For details see the paper at http://dx.doi.org/10.1007/s10994-010-5224-5.
Torch5 provides a matlab-like environment for state-of-the-art machinelearning algorithms. It is easy to use and provides a very efficient implementation, thanks to a easy and fast scripting language (Lua) and a underlying C++ implementation.
SURIKATA (Syntactic Universal Reasoning for Inducing Kolmogorov Abstract Theories Automatically) is a system for searching large spaces of artifacts and inducing algorithms for generating similar artifacts.
KeplerWeka adds the functionality of the open-source machinelearning and data mining workbench WEKA to the free and open-source, scientific workflow application, Kepler.
Onyx is for rapid prototyping and large-scale experimentation on advanced machine-learning algorithms with an emphasis on algorithms for online or streaming analysis, modeling, and classification.
CrfAny is a C++ package for efficient and exact training and inference of Conditional Random Fields over any graphical structure, supporting all feature types (boolean, integer and real) and command line, C++/Python Lib interfaces.
The Naval Postgraduate School MachineLearning Library. There are no official releases yet, but you can pull from the mercurial repository. See the wiki for help: https://sourceforge.net/apps/mediawiki/npsml/index.php?title=Main_Page
SAIM allows to interlink knowledge bases in the Semantic Web. It focuses on instance matching of very large knowledge bases available as SPARQL endpoints. SAIM uses machinelearning techniques and is compatible with SILK.
PhiWeave is a machinelearning library for structured prediction via factor graphs. It is part of an ongoing effort to implement and improve on the current state-of-the-art in inference and parameter estimation for graphical models.
BorderFlow implements a general-purpose graph clustering algorithm. It maximizes the inner to outer flow ratio from the border of each cluster to the rest of the graph.