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* Email: [mailto:besser82@fedoraproject.org besser82@fedoraproject.org]
 
* Email: [mailto:besser82@fedoraproject.org besser82@fedoraproject.org]
 
* Release notes owner: <!--- To be assigned by docs team [[User:FASAccountName| Release notes owner name]] <email address> -->
 
* Release notes owner: <!--- To be assigned by docs team [[User:FASAccountName| Release notes owner name]] <email address> -->
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== Current status ==
 
== Current status ==
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* further Information: [http://en.wikipedia.org/wiki/Shogun_%28toolbox%29 on Wikipedia]
 
* further Information: [http://en.wikipedia.org/wiki/Shogun_%28toolbox%29 on Wikipedia]
  
The machine learning toolbox's focus is on large scale kernel methods and especially on [http://en.wikipedia.org/wiki/Support_vector_machine Support Vector Machines (SVM)].  It provides a generic [http://en.wikipedia.org/wiki/Support_vector_machine SVM object] interfacing to several different [http://en.wikipedia.org/wiki/Support_vector_machine SVM implementations], among them the state of the art [http://en.wikipedia.org/wiki/LIBSVM LibSVM] and [http://svmlight.joachims.org/ SVMlight].  Each of the [http://en.wikipedia.org/wiki/Support_vector_machine 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 [http://en.wikipedia.org/wiki/Support_vector_machine 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.
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The machine learning toolbox's focus is on large scale kernel methods and especially on [http://en.wikipedia.org/wiki/Support_vector_machine Support Vector Machines (SVM)].  It provides a generic [http://en.wikipedia.org/wiki/Support_vector_machine SVM object] interfacing to several different [http://en.wikipedia.org/wiki/Support_vector_machine SVM implementations], among them the state of the art [http://en.wikipedia.org/wiki/LIBSVM LibSVM].  Each of the [http://en.wikipedia.org/wiki/Support_vector_machine 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 [http://en.wikipedia.org/wiki/Support_vector_machine 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.
  
 
== Benefit to Fedora ==
 
== Benefit to Fedora ==
<!-- What is the benefit to the platform?  If this is a major capability update, what has changed?  If this is a new functionality, what capabilities does it bring? Why will Fedora become a better distribution or project because of this proposal?-->
 
  
more to come soon.
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This will bring a Machine Learning Toolkit with a unique selection and versatility to Fedora.
  
 
== Scope ==
 
== Scope ==
<!-- What work do the developers have to accomplish to complete the change in time for release?  Is it a large change affecting many parts of the distribution or is it a very isolated change? What are those changes?-->
 
  
* Proposal owners:
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* Proposal owners: Create the rpm-spec and file a review bug. Have the package build after review was granted.
<!-- What work do the feature owners have to accomplish to complete the feature in time for release? Is it a large change affecting many parts of the distribution or is it a very isolated change? What are those changes?-->
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* Other developers: N/A (not a System Wide Change)
 
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* Release engineering: N/A (not a System Wide Change)
* Other developers: N/A (not a System Wide Change) <!-- REQUIRED FOR SYSTEM WIDE CHANGES -->
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* Policies and guidelines: N/A (not a System Wide Change)
<!-- What work do other developers have to accomplish to complete the feature in time for release?  Is it a large change affecting many parts of the distribution or is it a very isolated change? What are those changes?-->
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* Release engineering: N/A (not a System Wide Change) <!-- REQUIRED FOR SYSTEM WIDE CHANGES -->
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== Upgrade/compatibility impact ==
 
== Upgrade/compatibility impact ==
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N/A (not a System Wide Change)  
 
N/A (not a System Wide Change)  
  
 
== How To Test ==
 
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N/A (not a System Wide Change)  
  
 
== User Experience ==
 
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N/A (not a System Wide Change)  
 
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== Dependencies ==
 
== Dependencies ==
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== Contingency Plan ==
 
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== Documentation ==
 
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== Release Notes ==
 
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see Detailed Description
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Revision as of 18:28, 15 December 2013

Contents

The Shogun Machine Learning Toolbox

Summary

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.

Owner

Current status

  • Targeted release: Fedora 21
  • Last updated: 2013-09-24
  • Tracker bug: <will be assigned by the Wrangler>

Detailed Description

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.

Benefit to Fedora

This will bring a Machine Learning Toolkit with a unique selection and versatility to Fedora.

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)

Upgrade/compatibility impact

N/A (not a System Wide Change)

How To Test

N/A (not a System Wide Change)

User Experience

N/A (not a System Wide Change)

Dependencies

N/A (not a System Wide Change)

Contingency Plan

  • Contingency mechanism: N/A (not a System Wide Change)
  • Contingency deadline: N/A (not a System Wide Change)
  • Blocks release? N/A (not a System Wide Change)

Documentation

N/A (not a System Wide Change)

Release Notes

see Detailed Description