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Paketbeschreibung


Paketnameshogun-cmdline
BeschreibungLarge Scale Machine Learning Toolbox
Archiv/RepositoryOffizielles Debian Archiv squeeze (main)
Version0.9.3-4
Sektionscience
Prioritätoptional
Installierte Größe408 Byte
Hängt ab vonlibatlas3gf-base, libc6 (>= 2.3.6-6~), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-serial-1.8.
Empfohlene Pakete
PaketbetreuerSoeren Sonnenburg
Quelleshogun
Paketgröße45072 Byte
Prüfsumme MD537d83a9d914abedc65361f63c32056b0
Prüfsumme SHA1450c047a6ddca88eeec354b68fe4a1ad4572e323
Prüfsumme SHA25667a0490c2f0e0d645e48762a3660b984d0a74906d72f3a054ce7a9faded0c803
Link zum Herunterladenshogun-cmdline_0.9.3-4_i386.deb
Ausführliche BeschreibungSHOGUN - 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 Readline package.


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