The SHOGUN Machine Learning Toolbox

Sonnenburg, S; Rätsch, G; Henschel, S; Widmer, C; Behr, J; Zien, A; De Bona, F; Binder, A; Gehl, C; Franc, V

doi:10.5555/1756006.1859911

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The SHOGUN Machine Learning Toolbox

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Zien, A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Sonnenburg, S., Rätsch, G., Henschel, S., Widmer, C., Behr, J., Zien, A., et al. (2010). The SHOGUN Machine Learning Toolbox. Journal of Machine Learning Research, 11, 1799-1802. doi:10.5555/1756006.1859911.

Cite as: https://hdl.handle.net/21.11116/0000-000A-DDD0-2

Abstract

We have developed a machine learning toolbox, called SHOGUN, which is designed for unified large-scale learning for a broad range of feature types and learning settings. It offers a considerable number of machine learning models such as support vector machines, hidden Markov models, multiple kernel learning, linear discriminant analysis, and more. Most of the specific algorithms are able to deal with several different data classes. We have used this toolbox in several applications from computational biology, some of them coming with no less than 50 million training examples and others with 7 billion test examples. With more than a thousand installations worldwide, SHOGUN is already widely adopted in the machine learning community and beyond.

SHOGUN is implemented in C++ and interfaces to MATLABTM, R, Octave, Python, and has a stand-alone command line interface. The source code is freely available under the GNU General Public License, Version 3 at http://www.shogun-toolbox.org.