Information

Shogun - A Large Scale Machine Learning Toolbox

The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art OCAS, Liblinear, LibSVM, SVMLightSVMLin and GPDT. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels as e.g. the Locality Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts). For the latter the efficient LINADD optimizations are implemented. For linear SVMs the COFFIN framework allows for on-demand computing feature spaces on-the-fly, even allowing to mix sparse, dense and other data types. Furthermore, SHOGUN offers the freedom of working with custom pre-computed kernels. One of its key features is the combined kernel which can be constructed by a weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain. An optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Currently SVM one-class, 2-class and multiclass classification and regression problems can be dealt with. However SHOGUN also implements a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to train hidden markov models. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing.

SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave and Python and is proudly released as Machine Learning Open Source Software.


References

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S.Sonnenburg, G.Raetsch, and B.Schoelkopf. Large scale genomic sequence SVM classifiers. In Proceedings of the 22nd International Machine Learning Conference. ACM Press, 2005.
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S.Sonnenburg, G.Raetsch, A.Jagota, and K.-R. Mueller. New methods for splice-site recognition. In Proceedings of the International Conference on Artifical Neural Networks, 2002. Copyright by Springer.
S.Sonnenburg, A.Zien, and G.Raetsch. ARTS: Accurate Recognition of Transcription Starts in Human. 2006. (accepted).
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M.Kloft, U.Brefeldt, S.Sonnenburg, A.Zien, P.Laskov, K.-R. Mueller, Efficient and Accurate Lp-Norm Multiple Kernel Learning, Advances in Neural Information Processing Systems 21, MIT Press, Cambridge, MA,2009
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S. Sonnenburg, V. Franc. COFFIN: A Computational Framework for Linear SVMs. Proceedings of the 27nd International Machine Learning Conference, 2010.


What's New

Feb. 17, 2014 -> SHOGUN 3.2.0
Jan. 6, 2014 -> SHOGUN 3.1.1
Jan. 5, 2014 -> SHOGUN 3.1.0
Oct. 28, 2013 -> SHOGUN 3.0.0
March 17, 2013 -> SHOGUN 2.1.0
Sept. 1, 2012 -> SHOGUN 2.0.0
Dec. 1, 2011 -> SHOGUN 1.1.0