@article{SonRaeHenWidBehZieBonBinGehFra10, author = {S\"oren Sonnenburg and Gunnar R\"atsch and Sebastian Henschel and Christian Widmer and Jonas Behr and Alexander Zien and Fabio de Bona and Alexander Binder and Christian Gehl and Vojtech Franc}, title = {The {SHOGUN} Machine Learning Toolbox}, year = {2010}, journal = {Journal of Machine Learning Research}, volume = {11}, month = {June}, pages = {1799--1802}, url = {http://www.shogun-toolbox.org}, pdf = {http://www.jmlr.org/papers/volume11/sonnenburg10a/sonnenburg10a.pdf}, 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 for classification and regression, hidden Markov models, multiple kernel learning, linear discriminant analysis, linear programming machines, and perceptrons. Most of the specific algorithms are able to deal with several different data classes, including dense and sparse vectors and sequences using floating point or discrete data types. We have used this toolbox in several applications from computational biology, some of them coming with no less than 10 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 MATLAB, 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.} }