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.
| Tags | Scientific/Engineering Mathematics Bioinformatics Artificial Intelligence |
|---|---|
| Licenses | GPL |
| Operating Systems | POSIX Linux Cygwin Mac OS X |
| Implementation | Python C++ |
Recent releases
- 03 May 2009 06:42
Changes: This release contains several bugfixes and cleanups. Performance measures are now functional again. There have been build fixes for Mac OS X and documentation updates for the libshogun developer tutorial.
- 23 Mar 2009 14:38
Changes: This release contains several cleanups and enhancements. Shogun now supports all data types from python_modular: dense, scipy-sparse csc_sparse matrices and strings of type bool, char, (u)int{8,16,32,64}, and float{32,64,96}. In addition, individual vectors and strings can now be obtained and even changed. See examples/python_modular/features_*.py for examples. Now AUC maximization works with arbitrary kernel SVMs. Further documentation updates, polished examples, and bugfixes were made.
- 09 Mar 2009 06:54
Changes: This release contains several cleanups, feature enhancements, and bugfixes. The configure script has been improved to smoothe installation. The documentation and the tutorial on how to develop with libshogun were improved. The elwms (eilergendewollmilchsau) interface was added, which interfaces to Python, Octave, R, and Matlab in one file and provides the run_{octave,python,r} commands to run code from other languages transparently, making variables available to the target language and avoiding file I/O. The Custom Kernel no longer requires features nor initialization, even not when used in CombinedKernel.
- 20 Feb 2009 13:29
Changes: This release adds several drastic changes. Most importantly, shogun has been split into libshogun and libshogunui, multiple kernel learning (for classification) is now available via glpk, support has been added for "dotfeatures" which enable learning of linear classifiers with mixed datatypes, and shogun now runs on the iphone.
- 26 Nov 2008 14:22
Changes: This release contains several cleanups and bugfixes. The ambigous self-defined data types for char, int, float, and so on were replaced with "standardized" types. It fixes non-contiguous arrays and vectors in the Python modular interface. Improper assignment of labels in the constructor of WDSVMOcas was fixed. This problem led to segfaults on destruction in the (Python) modular interface. A segfault opportunity in MultiClassSVM was fixed.
