| Paketname | shogun-r |
| Beschreibung | Large Scale Machine Learning Toolbox |
| Archiv/Repository | Offizielles Debian Archiv squeeze (main) |
| Version | 0.9.3-4 |
| Sektion | science |
| Priorität | optional |
| Installierte Größe | 700 Byte |
| Hängt ab von | libatlas3gf-base, libc6 (>= 2.3.6-6~), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-serial-1.8. |
| Empfohlene Pakete | |
| Paketbetreuer | Soeren Sonnenburg |
| Quelle | shogun |
| Paketgröße | 67888 Byte |
| Prüfsumme MD5 | ef356e415304810720261cad5d63b0ca |
| Prüfsumme SHA1 | 3f03dc109510b430c0cb756ee80155075ae8e69f |
| Prüfsumme SHA256 | bb8e204049912fe4e494384024c761eef6ca75e743d8dad18daaf7c64a5f62b0 |
| Link zum Herunterladen | shogun-r_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 is the R
package.
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