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:

New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance

New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)

BibTeX Entry: Download

Supported Operating Systems: Linux, Macosx, Windows

Data Formats: Plain Ascii, Csv

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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