SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.License: GNU General Public License (GPL)Changes:
This release contains several major enhancements and bugfixes. Experimental support for the modular R interface was added. All python-modular examples have been ported to r-modular. The "send_command" legacy is no longer necessary; numbers can be used as such and don't have to be given as strings. All examples for R, Python, Octave, and Matlab have been converted to the new syntax. The command line interface has been resurrected and basic functionality was restored. The documentation was updated and a number of bugs have been fixed.

More...