The Journal of Machine Learning Research will now publish machine learning open source software (MLOSS). Complementing this effort we have been setting up mloss.org - a portal for any kind of machine learning open source software and ask you to contribute.

Software: I am the main author of the SHOGUN machine learning toolbox which started with a Hidden Markov Model to be used for Splice Site Classification back in 1999 and was since then continuously extended by Gunnar Rätsch and me. Now its focus is on large scale kernel methods, especially Support Vector Machines. It comes with a generic interface for SVMs, features several SVM and kernel implementations, includes LinAdd optimizations and also Multiple Kernel Learning algorithms. SHOGUN also implements a number of linear methods. It allows the input feature-objects to be dense, sparse or strings and of type int/short/double/char.
SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave and Python. A more exhaustive feature list can be found on its project page: http:www.shogun-toolbox.org
http://www.shogun-toolbox.org/