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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 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 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.
Author Information
Soeren Sonnenburg (TomTom)
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2011 Poster: Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation »
Nico Goernitz · Christian Widmer · Georg Zeller · Andre Kahles · Soeren Sonnenburg · Gunnar Rätsch -
2010 Workshop: New Directions in Multiple Kernel Learning »
Marius Kloft · Ulrich Rueckert · Cheng Soon Ong · Alain Rakotomamonjy · Soeren Sonnenburg · Francis Bach -
2009 Poster: Efficient and Accurate Lp-Norm Multiple Kernel Learning »
Marius Kloft · Ulf Brefeld · Soeren Sonnenburg · Pavel Laskov · Klaus-Robert Müller · Alexander Zien -
2008 Workshop: Machine Learning Open Source Software »
Soeren Sonnenburg · Mikio L Braun · Cheng Soon Ong -
2006 Workshop: Workshop On Machine Learning Open Source Software »
Soeren Sonnenburg -
2006 Poster: Large Scale Hidden Semi-Markov SVMs »
Gunnar Rätsch · Soeren Sonnenburg -
2006 Poster: Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees »
Konrad Rieck · Pavel Laskov · Soeren Sonnenburg -
2006 Demonstration: SHOGUN Machine Learning Toolbox »
Soeren Sonnenburg · Gunnar Rätsch