Connectionists: MDP release 3.0

[Tiziano Zito]

We are glad to announce release 3.0 of the Modular toolkit for Data
Processing (MDP). 

MDP is a Python library of widely used data processing algorithms
that can be combined according to a pipeline analogy to build more
complex data processing software. The base of available algorithms
includes signal processing methods (Principal Component Analysis,
Independent Component Analysis, Slow Feature Analysis),
manifold learning methods ([Hessian] Locally Linear Embedding),
several classifiers, probabilistic methods (Factor Analysis, RBM),
data pre-processing methods, and many others.

What's new in version 3.0?
--------------------------

- Python 3 support
- New extensions: caching and gradient
- Automatically generated wrappers for scikits.learn algorithms
- Shogun and libsvm wrappers
- New algorithms: convolution, several classifiers and several
  user-contributed nodes
- Several new examples on the homepage
- Improved and expanded tutorial
- Several improvements and bug fixes
- New license: MDP goes BSD!

Resources
---------
Download: http://sourceforge.net/projects/mdp-toolkit/files
Homepage: http://mdp-toolkit.sourceforge.net
Mailing list: http://lists.sourceforge.net/mailman/listinfo/mdp-toolkit-users

Acknowledgments
---------------
We thank the contributors to this release: Sven Dähne, Alberto Escalante,
Valentin Haenel, Yaroslav Halchenko, Sebastian Höfer, Michael Hull,
Samuel John, José Quesada, Ariel Rokem, Benjamin Schrauwen, David
Verstraeten, Katharina Maria Zeiner. 


The MDP developers,
Pietro Berkes
Zbigniew Jędrzejewski-Szmek
Rike-Benjamin Schuppner
Niko Wilbert
Tiziano Zito