shogun 0.10.0-2.1ubuntu1 source package in Ubuntu
Changelog
shogun (0.10.0-2.1ubuntu1) oneiric; urgency=low
* Build for python2.7.
* Build-depend on doxygen-latex.
shogun (0.10.0-2.1) unstable; urgency=low
* Non-maintainer upload.
* Apply upstream patch to fix FTBFS with new gcc (closes: #625115). Thanks,
Sebastian Ramacher!
shogun (0.10.0-2) unstable; urgency=low
* Upload to unstable.
shogun (0.10.0-1) experimental; urgency=low
* New upstream version with major feature enhancements.
- Fix build with ld --as-needed thanks Matthias Klose
for the patch. (Closes: #606018)
- Cherry pick patches from upstream svn-trunk to fix build failure.
-- Matthias Klose <email address hidden> Tue, 20 Sep 2011 13:07:03 +0200
Upload details
- Uploaded by:
- Matthias Klose
- Uploaded to:
- Oneiric
- Original maintainer:
- Soeren Sonnenburg
- Architectures:
- any
- Section:
- science
- Urgency:
- Low Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| shogun_0.10.0.orig.tar.gz | 3.1 MiB | 1815445601b5b2d3af0f3418b5416017ee3768bc941e976ce7b3a5c3e5856a2d |
| shogun_0.10.0-2.1ubuntu1.debian.tar.gz | 19.8 KiB | 87cf18b838607fe49277165b86c82d19e8761fca3a0ba8f094b223e2d4e554e9 |
| shogun_0.10.0-2.1ubuntu1.dsc | 1.8 KiB | 4ebae62c2a487356aba425daa61423c3319bb2df98ca3c11db2303fbbf967f97 |
Available diffs
- diff from 0.9.3-4ubuntu9 to 0.10.0-2.1ubuntu1 (344.2 KiB)
Binary packages built by this source
- libshogun-dev: Large Scale Machine Learning Toolbox
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.
- libshogun9: No summary available for libshogun9 in ubuntu oneiric.
No description available for libshogun9 in ubuntu oneiric.
- libshogunui-dev: No summary available for libshogunui-dev in ubuntu precise.
No description available for libshogunui-dev in ubuntu precise.
- libshogunui6: No summary available for libshogunui6 in ubuntu precise.
No description available for libshogunui6 in ubuntu precise.
- shogun-cmdline: No summary available for shogun-cmdline in ubuntu precise.
No description available for shogun-cmdline in ubuntu precise.
- shogun-dbg: Large Scale Machine Learning Toolbox
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.
- shogun-doc-cn: Large Scale Machine Learning Toolbox
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
Chinese user and developer documentation.
- shogun-doc-en: No summary available for shogun-doc-en in ubuntu oneiric.
No description available for shogun-doc-en in ubuntu oneiric.
- shogun-elwms: No summary available for shogun-elwms in ubuntu precise.
No description available for shogun-elwms in ubuntu precise.
- shogun-octave: No summary available for shogun-octave in ubuntu precise.
No description available for shogun-octave in ubuntu precise.
- shogun-octave-modular: No summary available for shogun-octave-modular in ubuntu precise.
No description available for shogun-
octave- modular in ubuntu precise.
- shogun-python: No summary available for shogun-python in ubuntu oneiric.
No description available for shogun-python in ubuntu oneiric.
- shogun-python-modular: Large Scale Machine Learning Toolbox
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.
- shogun-r: No summary available for shogun-r in ubuntu precise.
No description available for shogun-r in ubuntu precise.
