Download JAR files tagged by lowest with all dependencies
webforj-foundation from group com.webforj (version 24.10)
The Webforj foudation is the lowest-level moulde used by all the other layers of the Webforj framework.
It manages communication with BBj and provides core components.
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Artifact webforj-foundation
Group com.webforj
Version 24.10
Last update 23. July 2024
Organization not specified
URL Not specified
License not specified
Dependencies amount 3
Dependencies webforj-dispatcher, webforj-data, gson,
There are maybe transitive dependencies!
Group com.webforj
Version 24.10
Last update 23. July 2024
Organization not specified
URL Not specified
License not specified
Dependencies amount 3
Dependencies webforj-dispatcher, webforj-data, gson,
There are maybe transitive dependencies!
isotonicRegression from group nz.ac.waikato.cms.weka (version 1.0.2)
Learns an isotonic regression model. Picks the attribute that results in the lowest squared error. Missing values are not allowed. Can only deal with numeric attributes. Considers the monotonically increasing case as well as the monotonically decreasing case.
Group: nz.ac.waikato.cms.weka Artifact: isotonicRegression
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Show all versions Show documentation Show source
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Artifact isotonicRegression
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/isotonicRegression
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
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/isotonicRegression
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 1.0.2)
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.
Group: nz.ac.waikato.cms.weka Artifact: leastMedSquared
Show all versions Show documentation Show source
Show all versions Show documentation Show source
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Artifact leastMedSquared
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
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
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!
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