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udunits from group org.lasersonlab.thredds (version 5.0.0)
The ucar.units Java package is for decoding and encoding formatted unit specifications (e.g. "m/s"), converting numeric values between compatible units (e.g. between "m/s" and "knot"), and for performing arithmetic operations on units (e.g. dividing one unit by another, or raising a unit to a power).
Artifact udunits
Group org.lasersonlab.thredds
Version 5.0.0
Last update 27. July 2018
Organization not specified
URL https://github.com/lasersonlab/thredds
License (MIT-style) netCDF C library license
Dependencies amount 3
Dependencies joda-time, jsr305, slf4j-api,
There are maybe transitive dependencies!
Group org.lasersonlab.thredds
Version 5.0.0
Last update 27. July 2018
Organization not specified
URL https://github.com/lasersonlab/thredds
License (MIT-style) netCDF C library license
Dependencies amount 3
Dependencies joda-time, jsr305, slf4j-api,
There are maybe transitive dependencies!
udunits from group edu.ucar (version 4.5.5)
The ucar.units Java package is for decoding and encoding
formatted unit specifications (e.g. "m/s"), converting numeric values
between compatible units (e.g. between "m/s" and "knot"), and for
performing arithmetic operations on units (e.g. dividing one unit by
another, raising a unit to a power).
Artifact udunits
Group edu.ucar
Version 4.5.5
Last update 21. April 2015
Organization not specified
URL http://www.unidata.ucar.edu/software/udunits//
License not specified
Dependencies amount 2
Dependencies joda-time, jcip-annotations,
There are maybe transitive dependencies!
Group edu.ucar
Version 4.5.5
Last update 21. April 2015
Organization not specified
URL http://www.unidata.ucar.edu/software/udunits//
License not specified
Dependencies amount 2
Dependencies joda-time, jcip-annotations,
There are maybe transitive dependencies!
DTNB from group nz.ac.waikato.cms.weka (version 1.0.3)
Class for building and using a decision table/naive bayes hybrid classifier. At each point in the search, the algorithm evaluates the merit of dividing the attributes into two disjoint subsets: one for the decision table, the other for naive Bayes. A forward selection search is used, where at each step, selected attributes are modeled by naive Bayes and the remainder by the decision table, and all attributes are modelled by the decision table initially. At each step, the algorithm also considers dropping an attribute entirely from the model.
For more information, see:
Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), 318-319, 2008.
0 downloads
Artifact DTNB
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 30. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/DTNB
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 30. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/DTNB
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
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