SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). The project 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.
What's New in This Release: [ read full changelog ]
· This release contains a quite large number of bugfixes documentation updates (tutorials and a method overview are now available for C++ developers, with static and modular interfaces).
· Multiple Kernel Learning has been reworked, and works using interleaved optimization via SVMLight or the wrapper algorithm via any SVM like LibSVM for regression and one and two-class classification.
