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averagedOneDependenceEstimators from group nz.ac.waikato.cms.weka (version 1.2.1)
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks. For more information, see G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
Group: nz.ac.waikato.cms.weka Artifact: averagedOneDependenceEstimators
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Artifact averagedOneDependenceEstimators
Group nz.ac.waikato.cms.weka
Version 1.2.1
Last update 20. July 2012
Tags: aode webb have 2005 space wang resulting algorithm while naive detrimental over more weaker accurate highly less hence information computationally achieves boughton alternative bayes learning tasks aggregating dependence like machine models many that averaging than assumptions classification delivering independence estimators small efficient
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/averagedOneDependenceEstimators
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.2.1
Last update 20. July 2012
Tags: aode webb have 2005 space wang resulting algorithm while naive detrimental over more weaker accurate highly less hence information computationally achieves boughton alternative bayes learning tasks aggregating dependence like machine models many that averaging than assumptions classification delivering independence estimators small efficient
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/averagedOneDependenceEstimators
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
averagedOneDependenceEstimators from group nz.ac.waikato.cms.weka (version 1.0.2)
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks. For more information, see G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
Group: nz.ac.waikato.cms.weka Artifact: averagedOneDependenceEstimators
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Artifact averagedOneDependenceEstimators
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Tags: aode webb have 2005 space wang resulting algorithm while naive detrimental over more weaker accurate highly less hence information computationally achieves boughton alternative bayes learning tasks aggregating dependence like machine models many that averaging than assumptions classification delivering independence estimators small efficient
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/averagedOneDependenceEstimators
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: aode webb have 2005 space wang resulting algorithm while naive detrimental over more weaker accurate highly less hence information computationally achieves boughton alternative bayes learning tasks aggregating dependence like machine models many that averaging than assumptions classification delivering independence estimators small efficient
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/averagedOneDependenceEstimators
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
averagedOneDependenceEstimators from group nz.ac.waikato.cms.weka (version 1.0.1)
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks. For more information, see G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
Group: nz.ac.waikato.cms.weka Artifact: averagedOneDependenceEstimators
Show documentation Show source
Show documentation Show source
0 downloads
Artifact averagedOneDependenceEstimators
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 24. April 2012
Tags: aode webb have 2005 space wang resulting algorithm while naive detrimental over more weaker accurate highly less hence information computationally achieves boughton alternative bayes learning tasks aggregating dependence like machine models many that averaging than assumptions classification delivering independence estimators small efficient
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/averagedOneDependenceEstimators
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: aode webb have 2005 space wang resulting algorithm while naive detrimental over more weaker accurate highly less hence information computationally achieves boughton alternative bayes learning tasks aggregating dependence like machine models many that averaging than assumptions classification delivering independence estimators small efficient
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/averagedOneDependenceEstimators
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, weka-dev,
There are maybe transitive dependencies!
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