Download all versions of winnow JAR files with all dependencies
winnow from group nz.ac.waikato.cms.weka (version 1.0.2)
Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318; N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz. Does classification for problems with nominal attributes (which it converts into binary attributes)
Artifact winnow
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Tags: does binary irrelevant nominal algorithm more santa cruz when winnow converts algorithms information with linear mistake university problems littlestone california into logarithmic learning machine threshold bounds balanced which classification implements abound 1988 1989 attributes quickly
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/winnow
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: does binary irrelevant nominal algorithm more santa cruz when winnow converts algorithms information with linear mistake university problems littlestone california into logarithmic learning machine threshold bounds balanced which classification implements abound 1988 1989 attributes quickly
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/winnow
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
winnow from group nz.ac.waikato.cms.weka (version 1.0.1)
Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318; N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz. Does classification for problems with nominal attributes (which it converts into binary attributes)
Artifact winnow
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 24. April 2012
Tags: does binary irrelevant nominal algorithm more santa cruz when winnow converts algorithms information with linear mistake university problems littlestone california into logarithmic learning machine threshold bounds balanced which classification implements abound 1988 1989 attributes quickly
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/winnow
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 24. April 2012
Tags: does binary irrelevant nominal algorithm more santa cruz when winnow converts algorithms information with linear mistake university problems littlestone california into logarithmic learning machine threshold bounds balanced which classification implements abound 1988 1989 attributes quickly
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/winnow
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
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