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 brings a more mature Python modular
interface: it now contains a full fledged test
suite for all implemented methods, interactive
documentation, and toy examples describing
everything, the use of kernels, classifier,
distributions, features, distances, regression,
and preprocessors. The code is now doxygen
documented and many minor improvements (e.g.
reading strings directly from file) were added.
Several memory leaks and crashers have been fixed.
The WDSVMOcas method was added. SVMOCAS and
liblinear were updated, fixing minor problems.
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