sci@gentoo.org
Gentoo Science Project
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.