| Ausführliche Beschreibung | SHOGUN - is a new machine learning toolbox with focus on large scale kernel
methods and especially on Support Vector Machines (SVM) with focus to
bioinformatics. It provides a generic SVM object interfacing to several
different SVM implementations. Each of the SVMs can be combined with a variety
of the many kernels implemented. It can deal with weighted linear combination
of a number of sub-kernels, each of which not necessarily working on the same
domain, where an optimal sub-kernel weighting can be learned using Multiple
Kernel Learning. Apart from SVM 2-class classification and regression
problems, a number of linear methods like Linear Discriminant Analysis (LDA),
Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to
train hidden markov models are implemented. 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.
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SHOGUN comes in different flavours, a stand-a-lone version and also with
interfaces to Matlab(tm), R, Octave, Readline and Python. This is the English
user and developer documentation.
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