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SHOGUN - User manual
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About this document
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This is the user manual for the SHOGUN toolbox.

Introduction to SHOGUN
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The SHOGUN machine learning toolbox's focus is on 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[1] and SVMlight[2].  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[3], Fischer[4], TOP[5], Spectrum[6], Weighted Degree
Kernel (with shifts)[7]. For the latter the efficient LINADD[8]
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[9]. 
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 "preprocessors" (e.g. substracting the mean) can be attached to
each feature object allowing for on-the-fly pre-processing.
License: GPL version 2 or newer
URL: http://www.fml.tuebingen.mpg.de/raetsch/projects/shogun
