Libsvm

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(Created page with "__TOC__ * Web site: link * Described version: xx.xx * Licensing: e.g. LGPL 3, BSD... * User documentation: link * Download: link * Source code: link == Authors/Maintainers == *...")
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* Web site: link
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* Web site: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
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* Described version: xx.xx
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* Described version: 3,1
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* Licensing: e.g. LGPL 3, BSD...
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* Licensing: e.g. GPL
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* User documentation: link
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* User documentation: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
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* Download: link
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* Download: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
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* Source code: link
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* Source code: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
== Authors/Maintainers ==
== Authors/Maintainers ==
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* Also origin, if the software comes from a specific project.
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http://www.csie.ntu.edu.tw/~cjlin/libsvm/acknowledgements
== Summary ==
== Summary ==
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One paragraph description of purpose, targer area, approach.
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LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
== Features ==
== Features ==
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* Listed features
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* Different SVM formulations
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* Efficient multi-class classification
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* Cross validation for model selection
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* Probability estimates
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* Various kernels (including precomputed kernel matrix)
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* Weighted SVM for unbalanced data
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* Both C++ and Java sources
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* GUI demonstrating SVM classification and regression
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* Python, R, MATLAB, Perl, Ruby, Weka, Common LISP, CLISP, Haskell, LabVIEW, and PHP interfaces. C# .NET code and CUDA extension is available.
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* It's also included in some data mining environments: RapidMiner and PCP.
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* Automatic model selection which can generate contour of cross valiation accuracy.
== Architectural/Functional Overview ==
== Architectural/Functional Overview ==
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* high level design info, how it works, performance  - may be a link, or several links
 
== Usage Overview ==
== Usage Overview ==
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* If possible with small example - may be a link
 
== Dependacies ==
== Dependacies ==
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* list of all relevant dependencies on other libraries
 
== HP-SEE Applications ==
== HP-SEE Applications ==
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* Applications using it
 
== Resource Centers ==
== Resource Centers ==
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* RCs supporting it (with version number if not the same as above)
 
== Usage by Other Projects and Communities ==
== Usage by Other Projects and Communities ==
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* If any
 
== Recommendations for Configuration and Usage ==
== Recommendations for Configuration and Usage ==
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Please describe here any common settings, configurations or conventions that would make the usage of this resource (library or tool) more interoperable or scalable across the HP-SEE resources. These recommendations should include anything that is related to the resource and is agreed upon by administrators and users, or across sites and applications. These recommendations should emerge from questions or discussions opened by site administrators or application developers, at any stage, including installation, development, usage, or adaptation for another HPC centre.
 
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Provided descriptions should describe general or site specific aspects of resource installation, configuration and usage, or describe the guidelines or convention for deploying or using the resource within the local (user/site) or temporary environment (job). Examples are:
 
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* Common configuration settings of execution environment
 
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* Filesystem path or local access string
 
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* Environment variables to be set or used by applications
 
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* Options (e.g. additional modules) that are needed or required by applications and should be present
 
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* Minimum quantitative values (e.g. quotas) offered by the site
 
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* Location and format of some configuration or usage hint instructing applications on proper use of the resource or site specific policy
 
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* Key installation or configuration settings that should be set to a common value, or locally tweaked by local site admins
 
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* Conventions for application or job bound installation and usage of the resource
 

Revision as of 09:51, 23 June 2011

Contents


Authors/Maintainers

http://www.csie.ntu.edu.tw/~cjlin/libsvm/acknowledgements

Summary

LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

Features

  • Different SVM formulations
  • Efficient multi-class classification
  • Cross validation for model selection
  • Probability estimates
  • Various kernels (including precomputed kernel matrix)
  • Weighted SVM for unbalanced data
  • Both C++ and Java sources
  • GUI demonstrating SVM classification and regression
  • Python, R, MATLAB, Perl, Ruby, Weka, Common LISP, CLISP, Haskell, LabVIEW, and PHP interfaces. C# .NET code and CUDA extension is available.
  • It's also included in some data mining environments: RapidMiner and PCP.
  • Automatic model selection which can generate contour of cross valiation accuracy.

Architectural/Functional Overview

Usage Overview

Dependacies

HP-SEE Applications

Resource Centers

Usage by Other Projects and Communities

Recommendations for Configuration and Usage

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