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, SVMLight, SVMLin 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.

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J. Zhou, J. Chen and J. Ye. MALSAR: Multi-tAsk Learning via StructurAl Regularization. Arizona State University, 2012. http://www.public.asu.edu/~jye02/Software/MALSAR |

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K.Tsuda, M.Kawanabe, G.Raetsch, S.Sonnenburg, and K.R. Mueller. A new discriminative kernel from probabilistic models. Neural Computation, 14:2397--2414, 2002. |

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G.Raetsch and S.Sonnenburg. Accurate Splice Site Prediction for Caenorhabditis Elegans, pages 277-298. MIT Press series on Computational Molecular Biology. MIT Press, 2004. |

G.Raetsch, S.Sonnenburg, and B.Schoelkopf. RASE: recognition of alternatively spliced exons in c. elegans. Bioinformatics, 21:i369--i377, June 2005. |

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. |

S.Sonnenburg, G.Raetsch, and C.Schaefer. Learning interpretable SVMs for biological sequence classification. In RECOMB 2005, LNBI 3500, pages 389-407. Springer-Verlag Berlin Heidelberg, 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). |

S.Sonnenburg, G.Raetsch, C.Schaefer, and B.Schoelkopf,Large Scale Multiple Kernel Learning, Journal of Machine Learning Research, 2006, K.Bennett and E.P.-Hernandez Editors |

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. |

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 |