|
|
| Distribution |
Debian stable |
| Abteilung |
science |
| Quelle |
shogun |
| Version |
0.6.3-1 |
| Maintainer |
Soeren Sonnenburg <debian@nn7.de>
|
| 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.
|
| Abhängig von | libc6 (>= 2.7-1), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python (>= 2.4), python (< < 2.6), python-central (>= 0.6.7), python2.4 (>= 2.3.90), python2.5 (>= 2.5), libatlas3gf-base | liblapack.so.3gf | liblapack3gf, libatlas.so.3gf | libatlas3gf-base | | Recommends | python-matplotlib, python-numpy |
| Offizielle Seiten |
Paket
Entwicklerinformationen
Bugs (Binärpaket)
Bugs (Quellpaket) |
| Download |
amd64 |
|
|
|
