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Paketti: python-shogun-dbg (3.2.0-4 ja muut)

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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 package contains the debug symbols for the static and the modular Python interfaces.

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Imurointi kaikille saataville arkkitehtuureille
Arkkitehtuuri Versio Paketin koko Koko asennettuna Tiedostot
amd64 3.2.0-4 3,477.8 kt5,910.0 kt [tiedostoluettelo]
armel 3.2.0-4 3,589.9 kt6,329.0 kt [tiedostoluettelo]
armhf 3.2.0-4 3,614.2 kt6,354.0 kt [tiedostoluettelo]
i386 3.2.0-4 3,443.0 kt5,678.0 kt [tiedostoluettelo]
mips 3.2.0-4 3,574.3 kt6,681.0 kt [tiedostoluettelo]
mipsel 3.2.0-4 3,525.3 kt6,621.0 kt [tiedostoluettelo]
powerpc 3.2.0-4 3,603.0 kt6,077.0 kt [tiedostoluettelo]
s390x 3.2.0-4+b1 3,585.8 kt6,039.0 kt [tiedostoluettelo]