SHOGUN System
(Redirected from SHOGUN Toolbox)
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A SHOGUN System is an SVM-centric classification tool created and managed by ...
- AKA: Shogun Toolbox.
- Context:
- …
- Counter-Example(s):
- See: SVMlight Learning File Format.
References
2015
- http://www.shogun-toolbox.org/page/about/project_description
- QUOTE: The Shogun Machine learning toolbox provides a wide range of unified and efficient Machine Learning (ML) methods. The toolbox seamlessly allows to easily combine multiple data representations, algorithm classes, and general purpose tools. This enables both rapid prototyping of data pipelines and extensibility in terms of new algorithms. We combine modern software architecture in C++ with both efficient low-level computing backends and cutting edge algorithm implementations to solve large-scale Machine Learning problems (yet) on single machines.
One of Shogun's most exciting features is that you can use the toolbox through a unified interface from C++, Python, Octave, R, Java, Lua, C#, etc. This not just means that we are independent of trends in computing languages, but it also lets you use Shogun as a vehicle to expose your algorithm to multiple communities. We use SWIG to enable bidirectional communication between C++ and target languages. Shogun runs under Linux/Unix, MacOS, Windows.
Originally focussing on large-scale kernel methods and bioinformatics
- QUOTE: The Shogun Machine learning toolbox provides a wide range of unified and efficient Machine Learning (ML) methods. The toolbox seamlessly allows to easily combine multiple data representations, algorithm classes, and general purpose tools. This enables both rapid prototyping of data pipelines and extensibility in terms of new algorithms. We combine modern software architecture in C++ with both efficient low-level computing backends and cutting edge algorithm implementations to solve large-scale Machine Learning problems (yet) on single machines.
2010
- Sonnenburg, Sören, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, and Vojtěch Franc. "The SHOGUN machine learning toolbox." The Journal of Machine Learning Research 11 (2010): 1799-1802.