| Paketname | libshogunui-dev |
| Beschreibung | Large Scale Machine Learning Toolbox |
| Archiv/Repository | Offizielles Ubuntu Archiv lucid (universe) |
| Version | 0.9.1-1build1 |
| Sektion | universe/libdevel |
| Priorität | optional |
| Installierte Größe | 1304 Byte |
| Hängt ab von | libshogunui3 (= 0.9.1-1build1), libshogun-dev |
| Empfohlene Pakete | |
| Paketbetreuer | Ubuntu Developers |
| Quelle | shogun |
| Paketgröße | 318604 Byte |
| Prüfsumme MD5 | 70ac5e7918839ee9cbee30282afc295c |
| Prüfsumme SHA1 | 341bef0e0fd2c163a8ed501ca7ee18ed0f118332 |
| Prüfsumme SHA256 | 9aee84a3818291c2114824c53e4ce2c2ace51cd0c3370b979036c8f6696db442 |
| Link zum Herunterladen | libshogunui-dev_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 package
includes the developer files required to create stand-a-lone executables.
|