* It provides a generic SVM object interfacing to several different SVM
implementations, among them the state of the art LibSVM, SVMLight,
SVMLin and GPDT. Each of the SVMs can be combined with a variety of
kernels. The toolbox not only provides efficient implementations of the
most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid
Kernel but also comes with a number of recent string kernels as e.g. the
Locality Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with
shifts). For the latter the efficient LINADD optimizations are
* Also SHOGUN offers the freedom of working with custom pre-computed
kernels. One of its key features is the combined kernel which can be
constructed by a weighted linear combination of a number of sub-kernels,
each of which not necessarily working on the same domain. An optimal
sub-kernel weighting can be learned using Multiple Kernel Learning.
Currently SVM 2-class classification and regression problems can be
* SHOGUN also implements a number of linear methods like Linear
Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel)
Perceptrons and features algorithms to train hidden markov models.
* The input feature-objects can be dense, sparse or strings and of type
int/short/double/char and can be converted into different feature types.
Chains of preprocessors (e.g. substracting the mean) can be attached to
each feature object allowing for on-the-fly pre-processing.
* SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave
and Python and is proudly released as Machine Learning Open Source
Software ( http://mloss.org/ ) under the GPLv3+.