F21 Self Contained Change: The Shogun Machine Learning Toolbox

Jaroslav Reznik jreznik at redhat.com
Wed Apr 30 13:10:05 UTC 2014

= Proposed Self Contained Change: The Shogun Machine Learning Toolbox = 

Change owner(s): Björn Esser <besser82 at fedoraproject.org>

SHOGUN is a large Scale Machine Learning Toolbox, being implemented in C++ and 
offering interfaces to C#, Java, Lua, Octave, Perl, Python, R and Ruby. 

== Detailed Description ==
* Homepage: The SHOGUN Machine Learning Toolbox [1]
* SCM-repo: on GitHub [2]
* Documentation: is available here [3]
* further Information: on Wikipedia [4]

The 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. 

== Scope ==
* Proposal owners: Create the rpm-spec and file a review bug. Have the package 
build after review was granted.
* Other developers: N/A (not a System Wide Change)
* Release engineering: N/A (not a System Wide Change)
* Policies and guidelines: N/A (not a System Wide Change) 

[1] http://shogun-toolbox.org/
[2] https://github.com/shogun-toolbox/shogun
[3] http://shogun-toolbox.org/doc/en/current/
[4] http://en.wikipedia.org/wiki/Shogun_%28toolbox%29
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