Description:

MLDemos puts together a number of open-source libraries for machine learning in a single framework that allows to draw data, experiment and visualize the effects of training and model parameters.

The emphasis is put on the visualization and its main purpose is education and understanding of several classification, clustering, regression, dynamical systems and reinforcement learning algorithms. Different visualization modes allow to grasp different aspects of the results obtained by the algorithms.

The software allows to use more than 30 different algorithms (such as SVM/SVR, Boosting, Kernel PCA, ICA, LWPR, SEDS,...) and to compare their results or the way they behave when parameters are changed. A comprehensive list of the algorithms is available on the project homepage.

Attached are the Windows binaries, please refer to the website for the OSX/Linux versions and the source code.

Changes to previous version:

• Added a 'help' page, displaying specific implementation information on the algorithms selected in the info/statistics dialog, which has been renamed. It now provides information on each algorithm when selected.
• Added Reinforcement Learning (Reward Maximization) algorithms to the architecture. It is now possible to draw a reward function (using the +/- tool in the toolbar) and have the system display the maximization process.
• Added Random Search, Random Walk, PoWER, Genetic Algorithms and Particle Filters for reward maximization.
• Many structural changes on the source code (extracted common libraries to avoid redundant compiling). OpenCV is no longer needed by the main project but only by two plugins (PCAFaces and LinearMethods) which now link to OpenCV2.2.
• Bug fixes and small changes in the interface dynamics.
• The linux version is now packaged as a standalone program that works on all 2.6.X distributions

BibTeX Entry: Download

Supported Operating Systems: Linux, Macosx, Windows

Data Formats: Plain Ascii

Tags: Classification, Clustering, Regression, Support Vector Machines, Visualization, Machine Learning, Gaussian Mixture Models, Pegasos, Gaussian Processes, Multi Layer Perceptron, Relevant Vector Machines

Archive: download here

Comments

Thomas Wiecki (on March 23, 2011, 14:37:51)

Tried to compile under linux (kubuntu 10.10) which worked fine, but when I try to classify with any of the classifiers I get a segfault. Also tried windows binary with wine, but no avail.

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