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:

Pre-Summer update!

• Added the Comparison dialog. For each family of problems, it is possible to compare multiple algorithms or multiple parameters for the same algorithms on the data currently displayed in the canvas. Clicking the Compare button on the algorithm options panel adds the current parameters to the comparison battery. The Comparison dialog then allows to decide the number of cross-validation runs, ratio of training/testing samples and the visualization type (histograms or boxplots).

• Loading and running algorithms now works on multi-dimensional data (although only the first two dimensions are displayed)

• Added framework for multi-class classification (only implemented it for GMM and KNN for now)

• Added a projection display for Linear Projection methods, that now use naive bayes after projections. A "Set Projection/Set Source" button now allows to swap the samples in the canvas with the projected samples, and vice versa.

• Added Particle Swarm Optimization, Genetic Algorithms, Gradient Descent and Dan Grollman's Donut method for reward maximization.

• Added drag and drop buttons on the Maximization panel for painting gaussians and gradients on the reward map, or to drop standard benchmark problems for maximisation.

• Added Icons and Toolbar options (big/small/none)

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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