| Paketname | shogun-dbg |
| Beschreibung | Large Scale Machine Learning Toolbox |
| Archiv/Repository | Offizielles Debian Archiv squeeze (main) |
| Version | 0.9.3-4 |
| Sektion | debug |
| Priorität | extra |
| Installierte Größe | 80056 Byte |
| Hängt ab von | libshogun8 (= 0.9.3-4), libshogunui5 (= 0.9.3-4) |
| Empfohlene Pakete | |
| Paketbetreuer | Soeren Sonnenburg |
| Quelle | shogun |
| Paketgröße | 25776538 Byte |
| Prüfsumme MD5 | 9d85618c03c72294277a8c0fa030519b |
| Prüfsumme SHA1 | a89ac0a02c13c2e6d5713763ca1a3ad015f72644 |
| Prüfsumme SHA256 | b032f3c66b46cf0a68e1702be825c7cc6c521d594a128eaa00dc4ab5bf41274f |
| Link zum Herunterladen | shogun-dbg_0.9.3-4_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 package
contains debug symbols for all interfaces.
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