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:
The static R, Octave, Matlab, and Python interfaces have been rewritten from scratch, simplifying future extensions. They now use the same syntax and support the same set of commands. Toy examples describing the use of kernels, classifiers, distributions, features, distances, regression methods, and preprocessors for the static Python, R, Octave, and Matlab interface have been added. The user documentation has been improved. Support for ACML and Intel MKL and POIMs for the Python modular interface was added. Major memory leaks were fixed.
