view doc/v2_planning/existing_python_ml_libraries.txt @ 1036:89e76e6e074f

XG added to optimization team
author Xavier Glorot <glorotxa@iro.umontreal.ca>
date Tue, 07 Sep 2010 12:08:37 -0400
parents 6da3747c4c1f
children 730c00950957
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Committee members: GD, DWF, IG, DE

This committee will investigate the possibility of interfacing and/or
borrowing from other Python machine learning libraries that exist out there.
Some questions that we need to answer:

 * How much should we try to interface with other libraries? 
 * What parts can we and should we implement ourselves and what should we leave
 to the other libraries?

Preliminary list of libraries to look at:

 * Pybrain
 * MDP
 * Orange (http://www.ailab.si/orange/)
 * PyML (http://pyml.sourceforge.net/)
 * mlpy (https://mlpy.fbk.eu/)
 * APGL (http://packages.python.org/apgl/)
 * MontePython (http://montepython.sourceforge.net/)
 * Shogun python bindings
 * libsvm python bindings
 * scikits.learn

Also check out http://scipy.org/Topical_Software#head-fc5493250d285f5c634e51be7ba0f80d5f4d6443
- scipy.org's ``topical software'' section on Artificial Intelligence and
  Machine Learning