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.
|Tags||Scientific/Engineering Mathematics Bioinformatics Artificial Intelligence machine learning|
|Licenses||GPLv3 or later|
|Operating Systems||POSIX Linux Cygwin Mac OS X|
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.
Release Notes: This major update adds many improvements, new features, and bugfixes. It includes everything which has been carried out before and during the Google Summer of Code 2012. Students have implemented various new features such as structured output learning, gaussian processes, latent variable SVM (and structured output learning), statistical tests in kernel reproducing spaces, various multitask learning algorithms, and various usability improvements, to name a few.
Release Notes: This release introduced the concept of 'converters', which enables you to construct embeddings of arbitrary features. It also includes a few new dimension reduction techniques and significant performance improvements in the dimensionality reduction toolkit. Other improvements include a significant compilation speed-up, various bugfixes for modular interfaces and algorithms, and improved Cygwin, Mac OS X, and clang++ compatibility. Github Issues is now used for tracking bugs and issues.
Release Notes: This release features interfaces to new languages including Java, C#, Ruby, and Lua, a model selection framework, many dimension reduction techniques, Gaussian Mixture Model estimation, and a full-fledged online learning framework.