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.
License: GNU General Public License (GPL)
Changes:
This release fixes a memory leak in the libsvm
wrapper, re-enables error
checking in the matlab interface, frees memory
after batch computation,
fixes a parallel bug related to batch computation,
contains several
python-modular interface cleanups, and adds a fix
for the Chi2 kernel, a
Python build fix, and a double free fix for
combined kernels. gcc is now
used to generate build dependencies, and building
shogun on interix is
now possible. SVMOcas and GaussianShiftKernel
methods have been added.
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