Mercurial > pylearn
view doc/v2_planning/existing_python_ml_libraries.txt @ 1036:89e76e6e074f
XG added to optimization team
author | Xavier Glorot <glorotxa@iro.umontreal.ca> |
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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