shogun-elwms-static_1.1.0-6~nd70+1_i386.deb


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Description

shogun-elwms-static - Large Scale Machine Learning Toolbox

Property Value
Distribution Debian 8 (Jessie)
Repository NeuroDebian Main i386
Package name shogun-elwms-static
Package version 1.1.0
Package release 6~nd70+1
Package architecture i386
Package type deb
Installed size 203 B
Download size 58.59 KB
Official Mirror neurodebian.ovgu.de
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
eierlegendewollmilchsau package, providing interfaces and interoperability
commands to R, Octave and Python all at once.

Alternatives

Package Version Architecture Repository
shogun-elwms-static_1.1.0-6~nd70+1_amd64.deb 1.1.0 amd64 NeuroDebian Main
shogun-elwms-static - - -

Requires

Name Value
libarpack2 >= 2.1
libatlas3-base -
libc6 >= 2.3.6-6~
libgcc1 >= 1:4.1.1
libglpk0 >= 4.30
libhdf5-7 -
libjson0 >= 0.10
liblzma5 >= 5.1.1alpha+20110809
liblzo2-2 -
libpython2.7 >= 2.7
libshogun11 = 1.1.0-6~nd70+1
libstdc++6 >= 4.1.1
libxml2 >= 2.6.27
neurodebian-popularity-contest -
python >= 2.7
python << 2.8
python-numpy >= 1:1.6.1
python-numpy-abi9 -
r-base-core -
zlib1g >= 1:1.1.4

Conflicts

Name Value
shogun-elwms -

Replaces

Name Value
shogun-elwms -

Download

Type URL
Binary Package shogun-elwms-static_1.1.0-6~nd70+1_i386.deb
Source Package shogun

Install Howto

  1. Add the following line to /etc/apt/sources.list:
    deb http://neurodebian.ovgu.de/debian/ jessie main contrib non-free
  2. Install GPG key of the repository:
    # sudo apt-key adv --recv-keys --keyserver pgp.mit.edu 2649A5A9
  3. Update the package index:
    # sudo apt-get update
  4. Install shogun-elwms-static deb package:
    # sudo apt-get install shogun-elwms-static

See Also

Package Description
shogun-java-modular_1.1.0-6~nd70+1_i386.deb Large Scale Machine Learning Toolbox
shogun-lua-modular_1.1.0-6~nd70+1_i386.deb Large Scale Machine Learning Toolbox
shogun-python-modular_1.1.0-6~nd70+1_i386.deb Large Scale Machine Learning Toolbox
shogun-python-static_1.1.0-6~nd70+1_i386.deb Large Scale Machine Learning Toolbox
shogun-r-static_1.1.0-6~nd70+1_i386.deb Large Scale Machine Learning Toolbox
shogun-ruby-modular_1.1.0-6~nd70+1_i386.deb Large Scale Machine Learning Toolbox
sigviewer_0.5.1+svn556-3~nd80+1_i386.deb GUI viewer for biosignals such as EEG, EMG, and ECG
singularity-container_2.5.2-3~nd80+1_i386.deb container platform focused on supporting "Mobility of Compute"
spm8-common_8.5236~dfsg.1-1~nd70+1_all.deb analysis of brain imaging data sequences
spm8-data_8.5236~dfsg.1-1~nd70+1_all.deb data files for SPM8
spm8-doc_8.5236~dfsg.1-1~nd70+1_all.deb manual for SPM8
spyder_2.2.5+dfsg-1~nd80+1_all.deb python IDE for scientists
spykeviewer_0.4.4-1~nd80+1_all.deb graphical utility for analyzing electrophysiological data
stabilitycalc_0.1-1~nd70+1_all.deb evaluate fMRI scanner stability
stimfit_0.15.4-1~nd80+1_i386.deb Program for viewing and analyzing electrophysiological data
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