Description:

The SHOGUN machine learning toolbox's 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, among them the
state of the art LibSVM. 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 implemented. 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 dealt with. However 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 "pre-processors" (e.g. subtracting the
mean) can be attached to each feature object allowing for on-the-fly
pre-processing.

This build comes WITHOUT support for Thorsten Joachim's `SVM^light`, because
of it's 'no-redistribute', 'no-commercial-use' license.

This package contains the Octave-plugin for shogun.