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

Attached are the Windows binaries, please refer to the website for the mac version and the source code.

NOTE: Kernel K-Means was reported to not behave as expected in the linux version.

Changes to previous version:

• Introduced Plugins: Algorithms and InputOutput
• Algorithms are now accessed through 4 different interfaces (Classifiers, Clusterers, Regressors, Dynamical Systems) in the form of plugins. The software loads all the .dll / .so / .dylib found in the 'plugins' folder at runtime, and populates the graphical interface with their respective option panels. This makes it quite easy to implement additional algorithms, look at the examples provided in the sources for a template. It is possible to combine multiple algorithms into a collection of plugins, (e.g. to allow for a single algorithm to propose regression, classification and clustering).
• InputOutput plugins allow to send data to the software (replacing the current dataset) as well as sending requests for classification, clustering, regression or dynamical systems responses. The plugin then fetches the results when they are obtained. The algorithm is trained using the parameters currently selected in the graphical interface. The RandomEmitter plugin shows an extremely basic implementation as a template.
• Added Kernel PCA (under the Classification tab for the time being, will probably move to somewhere else at some point)
• Added PCAFaces, an InputOutput plugin that uses a webcam (or images) to collect image samples and performs PCA to extract 2 principal components
• Fixed and improved the background rendering of classification and dynamical system maps
• Fixed display of GMR model information
• Replaced the drawing toolbar with a simpler toolbar with context menus to change the tool's parameters
• Discontinued the VStudio project files (shouldn't be a big issue recreating the project), as I am now working entirely with QtCreator

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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