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Paketbeschreibung


Paketnameshogun-octave-modular
BeschreibungLarge Scale Machine Learning Toolbox
Archiv/RepositoryOffizielles Debian Archiv squeeze (main)
Version0.9.3-4
Sektionscience
Prioritätoptional
Installierte Größe17988 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
Paketgröße4960120 Byte
Prüfsumme MD55254764788a22ff4569a4772c23629c8
Prüfsumme SHA1b6c4266a6aa6f2c9f8021e4dd39635976a2c32a3
Prüfsumme SHA256923c7c65396ac96cb8ec1b705ea1defa6ab235522c9901466bc878ed52c0ac81
Link zum Herunterladenshogun-octave-modular_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 modular Octave package employing swig.


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