Ubuntu
shogun package

shogun 3.1.0-1 source package in Ubuntu

Changelog

shogun (3.1.0-1) unstable; urgency=low


  * New upstream version
  * Bump standards version to 3.9.5 (no changes required).

 -- Soeren Sonnenburg <email address hidden>  Sun, 05 Jan 2014 08:34:52 +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 Pocket Published Component Section

Downloads

File Size SHA-256 Checksum
shogun_3.1.0-1.dsc 2.4 KiB 79a5bde388b0515419d8ec2350493ca256396cb6b450657bb4b4fb10dcbcb6c0
shogun_3.1.0.orig.tar.xz 3.7 MiB bf4b889ace159d3314614684d0107106a02984069d00fdd5157dd9b0339940e1
shogun_3.1.0-1.debian.tar.gz 13.8 KiB a24157023304b571f93e3b6fa9ae0380014c88890306a08a291decc3cfb2f1c5

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: 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 core
 library with the machine learning methods and ui helpers all interfaces are
 based on.

shogun-cmdline-static: 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 Readline
 package.

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.