Paketname | shogun-python-modular |
Beschreibung | Large Scale Machine Learning Toolbox |
Archiv/Repository | Offizielles Ubuntu Archiv lucid (universe) |
Version | 0.9.1-1build1 |
Sektion | universe/science |
Priorität | optional |
Installierte Größe | 11324 Byte |
Hängt ab von | libatlas3gf-base | liblapack3gf | liblapack.so.3gf, libatlas3gf-base | libatlas.so.3gf, libc6 (>= 2. |
Empfohlene Pakete | python-numpy, python-matplotlib, python-scipy |
Paketbetreuer | Ubuntu Developers |
Quelle | shogun |
Paketgröße | 2487844 Byte |
Prüfsumme MD5 | 846566f6bdee62c8954488428ca91a92 |
Prüfsumme SHA1 | fa30b4dc213626660b30e97c3369c4e97d1739d2 |
Prüfsumme SHA256 | c6e750c76d6ff7e64c33f5ff40741d2c7452f2fc11d37b9d95cb40f4a8066c8f |
Link zum Herunterladen | shogun-python-modular_0.9.1-1build1_i386.deb |
Ausführliche Beschreibung | 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 is the modular
Python package employing swig.
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