Paper Info
Title
The SHOGUN Machine Learning Toolbox
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.
Year
DOI
Venue
2010
10.5555/1756006.1859911
Journal of Machine Learning Research
Keywords
Field
DocType
shogun machine learning toolbox,feature type,support vector machine,multiple kernel learning,considerable number,broad range,computational biology,different data class,billion test example,unified large-scale learning,gnu general public license,machine learning
Command-line interface,Source code,Computer science,Multiple kernel learning,Toolbox,Support vector machine,Shogun,Artificial intelligence,Hidden Markov model,Machine learning,Python (programming language)
Journal
Volume
ISSN
Citations 
11,
1532-4435
116
PageRank 
References 
Authors
19.53
6
10
Search Limit
100116
Name
Order
Citations
PageRank
Sören Sonnenburg11556125.12
Gunnar Rätsch25625671.20
Sebastian Henschel311619.53
Christian Widmer423927.29
Jonas Behr526927.72
Alexander Zien61255146.93
Fabio De Bona715628.91
Alexander Binder811619.53
Christian Gehl912419.98
Vojtěch Franc1058455.78