Package: libshogunui5 (0.9.3-4)

[ Source: shogun  ]

Large Scale Machine Learning Toolbox

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.

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 interface library that all static interfaces use.

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Download for all available architectures

Architecture	Package Size	Installed Size	Files
amd64	227.1 kB	712.0 kB	[list of files]
armel	223.2 kB	672.0 kB	[list of files]
i386	231.1 kB	712.0 kB	[list of files]
ia64	299.5 kB	1,288.0 kB	[list of files]
kfreebsd-amd64	228.2 kB	702.0 kB	[list of files]
kfreebsd-i386	223.5 kB	686.0 kB	[list of files]
mips	209.9 kB	836.0 kB	[list of files]
mipsel	202.4 kB	816.0 kB	[list of files]
powerpc	230.9 kB	764.0 kB	[list of files]
s390	220.3 kB	724.0 kB	[list of files]
sparc	225.9 kB	744.0 kB	[list of files]