Projects / SHOGUN

SHOGUN

SHOGUN is a machine learning toolbox whose 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, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.

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RSS Recent releases

  •  17 Feb 2014 20:04

Release Notes: This is mostly a bugfix release. Shogun now fully supports Python 3, will work with the upcoming Swig 3.0, and compiles cleanly with Octave 3.8 and Oracle/Open-jdk.

  •  09 Jan 2014 22:30

Release Notes: A compilation error occurring with CXX0X was fixed. The data version was bumped to the required version.

Release Notes: This release contains mostly bugfixes, but also feature enhancements. Most important, a couple of memory leaks related to apply() have been fixed. Writing and reading of shogun features as protobuf objects is now possible. Custom Kernel Matrices can now be 2^31-1 * 2^31-1 in size. Multiclass ipython notebooks were added, and the others improved. Leave-one-out crossvalidation is now conveniently supported.

Release Notes: This release switches the build system to using CMake. It adds some fancy interactive demos and ipython notebooks that you can also run in the cloud (see the links for further stats). There are other new features and many internal improvements, bugfixes, and documentation improvements. To speak in numbers, this release merges more than 2000 commits changing almost 400000 lines in more than 7000 files, and increases the number of unit tests from 50 to 600.

Release Notes: This release contains over 800 commits since 2.0.0 with a load of bugfixes, new features, and improvements that make Shogun more efficient, robust, and versatile. In particular, there is an initial alpha version of a Perl modular interface, Linear Time MMD on Streaming Data, a new structured output solver, and support for tapkee, a dimension reduction framework.

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