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Paketbeschreibung


Paketnameshogun-elwms
BeschreibungLarge Scale Machine Learning Toolbox
Archiv/RepositoryOffizielles Debian Archiv squeeze (main)
Version0.9.3-4
Sektionscience
Prioritätoptional
Installierte Größe648 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
Paketgröße111096 Byte
Prüfsumme MD5b00e765f08a32d3776d583cd5bf89b55
Prüfsumme SHA193a83f968988de11b25252b24506db77e433ed56
Prüfsumme SHA256c8d283ef545268a8f384892d63abfba5216ce1a633882862bf354854f768447d
Link zum Herunterladenshogun-elwms_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 eierlegendewollmilchsau package, providing interfaces and interoperability commands to R, Octave and Python all at once.


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