Fedora 20 Update: shogun-3.1.0-0.1.git20131212.70e774d.fc20

[updates at fedoraproject.org]

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Fedora Update Notification
FEDORA-2013-23513
2013-12-17 17:49:13
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Name        : shogun
Product     : Fedora 20
Version     : 3.1.0
Release     : 0.1.git20131212.70e774d.fc20
URL         : http://shogun-toolbox.org
Summary     : Large Scale Machine Learning Toolbox
Description :

The SHOGUN machine learning toolbox's focus is on large scale kernel methods
and especially on Support Vector Machines (SVM).  It provides a generic SVM
object interfacing to several different SVM implementations, among them the
state of the art LibSVM.  Each of the SVMs can be combined with a variety of
kernels.  The toolbox not only provides efficient implementations of the most
common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but
also comes with a number of recent string kernels as e.g. the Locality
Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts).  For
the latter the efficient LINADD optimizations are implemented.  Also SHOGUN
offers the freedom of working with custom pre-computed kernels.  One of its
key features is the "combined kernel" which can be constructed by a weighted
linear combination of a number of sub-kernels, each of which not necessarily
working on the same domain.  An optimal sub-kernel weighting can be learned
using Multiple Kernel Learning.  Currently SVM 2-class classification and
regression problems can be dealt with.  However SHOGUN also implements a
number of linear methods like Linear Discriminant Analysis (LDA), Linear
Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to
train hidden Markov-models.  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 "pre-processors" (e.g. subtracting the
mean) can be attached to each feature object allowing for on-the-fly
pre-processing.

SHOGUN is implemented in C++ and offers interfaces for
CLI

, Lua

, Octave

, Python


, Ruby.

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Update Information:

The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art LibSVM. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels as e.g. the Locality Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts). For the latter the efficient LINADD optimizations are implemented. Also SHOGUN offers the freedom of working with custom pre-computed kernels. One of its key features is the "combined kernel" which can be constructed by a weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain. An optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Currently SVM 2-class classification and regression problems can be dealt with. However SHOGUN also implements a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to train hidden Markov-models. 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 "pre-processors" (e.g. subtracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. SHOGUN is implemented in C++ and offers interfaces for CLI , Lua , Octave , Python , Ruby.
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ChangeLog:

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References:

  [ 1 ] Bug #1043283 - Review Request: shogun - Large Scale Machine Learning Toolbox
        https://bugzilla.redhat.com/show_bug.cgi?id=1043283
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This update can be installed with the "yum" update program.  Use 
su -c 'yum update shogun' at the command line.
For more information, refer to "Managing Software with yum",
available at http://docs.fedoraproject.org/yum/.

All packages are signed with the Fedora Project GPG key.  More details on the
GPG keys used by the Fedora Project can be found at
https://fedoraproject.org/keys
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