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:
Shogun now fully support the octave-modular interface. All python-modular examples describing the use of kernels, classifier, distributions, features, distances, regression, and preprocessors have been ported to octave-modular. The documentation received minor updates. The swig director has been unconditionally disabled, which reduces wrapper code size and compile time and also speeds up calls to virtual functions significantly. Big speed improvements are to be expected if you were using the python-modular interface. Command-line help was improved.
