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Paketbeschreibung


Paketnameshogun-python-modular
BeschreibungLarge Scale Machine Learning Toolbox
Archiv/RepositoryOffizielles Ubuntu Archiv lucid (universe)
Version0.9.1-1build1
Sektionuniverse/science
Prioritätoptional
Installierte Größe11324 Byte
Hängt ab vonlibatlas3gf-base | liblapack3gf | liblapack.so.3gf, libatlas3gf-base | libatlas.so.3gf, libc6 (>= 2.
Empfohlene Paketepython-numpy, python-matplotlib, python-scipy
PaketbetreuerUbuntu Developers
Quelleshogun
Paketgröße2487844 Byte
Prüfsumme MD5846566f6bdee62c8954488428ca91a92
Prüfsumme SHA1fa30b4dc213626660b30e97c3369c4e97d1739d2
Prüfsumme SHA256c6e750c76d6ff7e64c33f5ff40741d2c7452f2fc11d37b9d95cb40f4a8066c8f
Link zum Herunterladenshogun-python-modular_0.9.1-1build1_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 Python package employing swig.


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