Download all versions of SVMAttributeEval JAR files with all dependencies
SVMAttributeEval from group nz.ac.waikato.cms.weka (version 1.0.2)
Evaluates the worth of an attribute by using an SVM classifier. Attributes are ranked by the square of the weight assigned by the SVM. Attribute selection for multiclass problems is handled by ranking attributes for each class seperately using a one-vs-all method and then "dealing" from the top of each pile to give a final ranking.
For more information see:
I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46:389-422.
Artifact SVMAttributeEval
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Tags: using ranked weight give 2002 dealing attribute from handled more vector vapnik weston pile barnhill worth information final gene support problems assigned class learning machine selection machines multiclass seperately then classification evaluates classifier method each attributes square ranking guyon cancer
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/SVMAttributeEval
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Tags: using ranked weight give 2002 dealing attribute from handled more vector vapnik weston pile barnhill worth information final gene support problems assigned class learning machine selection machines multiclass seperately then classification evaluates classifier method each attributes square ranking guyon cancer
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/SVMAttributeEval
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
SVMAttributeEval from group nz.ac.waikato.cms.weka (version 1.0.1)
Evaluates the worth of an attribute by using an SVM classifier. Attributes are ranked by the square of the weight assigned by the SVM. Attribute selection for multiclass problems is handled by ranking attributes for each class seperately using a one-vs-all method and then "dealing" from the top of each pile to give a final ranking.
For more information see:
I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46:389-422.
Artifact SVMAttributeEval
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 24. April 2012
Tags: using ranked weight give 2002 dealing attribute from handled more vector vapnik weston pile barnhill worth information final gene support problems assigned class learning machine selection machines multiclass seperately then classification evaluates classifier method each attributes square ranking guyon cancer
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/SVMAttributeEval
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 24. April 2012
Tags: using ranked weight give 2002 dealing attribute from handled more vector vapnik weston pile barnhill worth information final gene support problems assigned class learning machine selection machines multiclass seperately then classification evaluates classifier method each attributes square ranking guyon cancer
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/SVMAttributeEval
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, weka-dev,
There are maybe transitive dependencies!
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