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.
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What's New in This Release: [ read full changelog ]
· This version 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.
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