Download all versions of leastMedSquared JAR files with all dependencies
leastMedSquared from group nz.ac.waikato.cms.weka (version 1.0.2)
Implements a least median squared linear regression utilizing the existing weka LinearRegression class to form predictions. Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model.
The basis of the algorithm is
Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection.
Artifact leastMedSquared
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Tags: model predictions squared basis meadian least data existing regression annick chosen from algorithm leroy outlier final functions robust linear with form error peter weka random class linearregression median detection 1987 generated rousseeuw subsamples implements lowest utilizing
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/leastMedSquared
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: model predictions squared basis meadian least data existing regression annick chosen from algorithm leroy outlier final functions robust linear with form error peter weka random class linearregression median detection 1987 generated rousseeuw subsamples implements lowest utilizing
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/leastMedSquared
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
leastMedSquared from group nz.ac.waikato.cms.weka (version 1.0.1)
Implements a least median squared linear regression utilizing the existing weka LinearRegression class to form predictions. Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model.
The basis of the algorithm is
Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection.
Artifact leastMedSquared
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 24. April 2012
Tags: model predictions squared basis meadian least data existing regression annick chosen from algorithm leroy outlier final functions robust linear with form error peter weka random class linearregression median detection 1987 generated rousseeuw subsamples implements lowest utilizing
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/leastMedSquared
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: model predictions squared basis meadian least data existing regression annick chosen from algorithm leroy outlier final functions robust linear with form error peter weka random class linearregression median detection 1987 generated rousseeuw subsamples implements lowest utilizing
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/leastMedSquared
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, weka-dev,
There are maybe transitive dependencies!
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