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SVMAttributeEval from group nz.ac.waikato.cms.weka (version 1.0.2)

Evaluates the worth of an attribute by using an SVM classifier. Attributes are ranked by the square of the weight assigned by the SVM. Attribute selection for multiclass problems is handled by ranking attributes for each class seperately using a one-vs-all method and then "dealing" from the top of each pile to give a final ranking. For more information see: I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46:389-422.

Group: nz.ac.waikato.cms.weka Artifact: SVMAttributeEval
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Artifact SVMAttributeEval
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/SVMAttributeEval
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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SPegasos from group nz.ac.waikato.cms.weka (version 1.0.2)

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. Can either minimize the hinge loss (SVM) or log loss (logistic regression). For more information, see S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.

Group: nz.ac.waikato.cms.weka Artifact: SPegasos
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Artifact SPegasos
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/SPegasos
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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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)

Group: nz.ac.waikato.cms.weka Artifact: winnow
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Artifact winnow
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/winnow
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

tertius from group nz.ac.waikato.cms.weka (version 1.0.2)

Finds rules according to confirmation measure (Tertius-type algorithm). For more information see: P. A. Flach, N. Lachiche (1999). Confirmation-Guided Discovery of first-order rules with Tertius. Machine Learning. 42:61-95.

Group: nz.ac.waikato.cms.weka Artifact: tertius
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Artifact tertius
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/tertius
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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tabuAndScatterSearch from group nz.ac.waikato.cms.weka (version 1.0.2)

Search methods contributed by Adrian Pino (ScatterSearchV1, TabuSearch). ScatterSearch: Performs an Scatter Search through the space of attribute subsets. Start with a population of many significants and diverses subset stops when the result is higher than a given treshold or there's not more improvement. For more information see: Felix Garcia Lopez (2004). Solving feature subset selection problem by a Parallel Scatter Search. Elsevier. Tabu Search: Abdel-Rahman Hedar, Jue Wangy, Masao Fukushima (2006). Tabu Search for Attribute Reduction in Rough Set Theory.

Group: nz.ac.waikato.cms.weka Artifact: tabuAndScatterSearch
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Artifact tabuAndScatterSearch
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/tabuAndScatterSearch
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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rotationForest from group nz.ac.waikato.cms.weka (version 1.0.3)

An ensemble learning method inspired by bagging and random sub-spaces. Trains an ensemble of decision trees on random subspaces of the data, where each subspace has been transformed using principal components analysis.

Group: nz.ac.waikato.cms.weka Artifact: rotationForest
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Artifact rotationForest
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/rotationForest
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

probabilisticSignificanceAE from group nz.ac.waikato.cms.weka (version 1.0.2)

Evaluates the worth of an attribute by computing the Probabilistic Significance as a two-way function (attribute-classes and classes-attribute association). For more information see: Amir Ahmad, Lipika Dey (2004). A feature selection technique for classificatory analysis.

Group: nz.ac.waikato.cms.weka Artifact: probabilisticSignificanceAE
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Artifact probabilisticSignificanceAE
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/probabilisticSignificanceAE
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

ordinalStochasticDominance from group nz.ac.waikato.cms.weka (version 1.0.2)

An implementation of the Ordinal Stochastic Dominance Learner. Further information regarding the OSDL-algorithm can be found in: S. Lievens, B. De Baets, K. Cao-Van (2006). A Probabilistic Framework for the Design of Instance-Based Supervised Ranking Algorithms in an Ordinal Setting. Annals of Operations Research; Kim Cao-Van (2003). Supervised ranking: from semantics to algorithms; Stijn Lievens (2004). Studie en implementatie van instantie-gebaseerde algoritmen voor gesuperviseerd rangschikken

Group: nz.ac.waikato.cms.weka Artifact: ordinalStochasticDominance
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Artifact ordinalStochasticDominance
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/ordinalStochasticDominance
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

ordinalLearningMethod from group nz.ac.waikato.cms.weka (version 1.0.2)

An implementation of the Ordinal Learning Method (OLM). Further information regarding the algorithm and variants can be found in: Arie Ben-David (1992). Automatic Generation of Symbolic Multiattribute Ordinal Knowledge-Based DSSs: methodology and Applications. Decision Sciences. 23:1357-1372.

Group: nz.ac.waikato.cms.weka Artifact: ordinalLearningMethod
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Artifact ordinalLearningMethod
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/ordinalLearningMethod
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

normalize from group nz.ac.waikato.cms.weka (version 1.0.2)

An instance filter that normalize instances considering only numeric attributes and ignoring class index

Group: nz.ac.waikato.cms.weka Artifact: normalize
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Artifact normalize
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/normalize
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

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