Paketname | libshogun6 |
Beschreibung | Large Scale Machine Learning Toolbox |
Archiv/Repository | Offizielles Ubuntu Archiv lucid (universe) |
Version | 0.9.1-1build1 |
Sektion | universe/libs |
Priorität | optional |
Installierte Größe | 1888 Byte |
Hängt ab von | libatlas3gf-base | liblapack3gf | liblapack.so.3gf, libatlas3gf-base | libatlas.so.3gf, libc6 (>= 2. |
Empfohlene Pakete | |
Paketbetreuer | Ubuntu Developers |
Quelle | shogun |
Paketgröße | 734436 Byte |
Prüfsumme MD5 | f6ffccf2ad234ddddc6c8604964546bb |
Prüfsumme SHA1 | 9e013751a4f8b0e73a48089ffaf8bf32840da0ef |
Prüfsumme SHA256 | 11af11b7c5fe828728e026bb26b91f2e85ba840ada4f22d8e59ffedfe0e8be6e |
Link zum Herunterladen | libshogun6_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 core
library all interfaces are based on.
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