@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.}
}