shogun 3.1.1-1 source package in Ubuntu
Changelog
shogun (3.1.1-1) unstable; urgency=low * New upstream version -- Soeren Sonnenburg <email address hidden> Sun, 05 Jan 2014 09:35:46 +0100
Upload details
- Uploaded by:
- Soeren Sonnenburg
- Uploaded to:
- Sid
- Original maintainer:
- Soeren Sonnenburg
- Architectures:
- any all
- Section:
- science
- Urgency:
- Low Urgency
See full publishing history Publishing
Series | Published | Component | Section | |
---|---|---|---|---|
Trusty | release | universe | science |
Downloads
File | Size | SHA-256 Checksum |
---|---|---|
shogun_3.1.1-1.dsc | 2.4 KiB | 15dc4522bb8301841d23818f98ca34c76a33eb47967299c366c9f59eb8221283 |
shogun_3.1.1.orig.tar.xz | 4.5 MiB | 4d8b8dae90a0c13a0ccea04779e583da0a90d4d39b140fd1482693120cf5c54f |
shogun_3.1.1-1.debian.tar.gz | 13.8 KiB | 6e664a6fb46c4d8ceb171dd6d0e28f88ce046eb11da7c21b2007ac9315efa755 |
Available diffs
- diff from 3.0.1~git20131115.557741b-2 to 3.1.1-1 (119.8 KiB)
- diff from 3.1.0-1 to 3.1.1-1 (32.2 KiB)
No changes file available.
Binary packages built by this source
- libshogun-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.
- 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.
- libshogun15: No summary available for libshogun15 in ubuntu utopic.
No description available for libshogun15 in ubuntu utopic.
- shogun-cmdline-static: No summary available for shogun-cmdline-static in ubuntu utopic.
No description available for shogun-
cmdline- static in ubuntu utopic.
- 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: 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 English
user and developer documentation.