Project details for MLDemos

Screenshot MLDemos 0.3.1

by basilio - March 24, 2011, 16:08:51 CET [ Project Homepage BibTeX Download ]

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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 and dynamical systems 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 panning and zooming capability, the system is no longer limited to a 0-1x0-1 subspace.
  • Added display of a Grid
  • Added obstacle avoidance for dynamical systems (currently one method implemented, paper in publication), found on the dynamical systems option
  • Cosmetic changes on the option panels (unified the different algorithm families)
  • Cosmetic changes on the source code (unified program and plugins projects into a single project)
  • Some bug fixes here and there
  • Optimized computations for the display of density maps, should be a tad faster on most machines now
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


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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