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probabilityCalibrationTrees from group nz.ac.waikato.cms.weka (version 1.0.0)

Provides probability calibration trees (PCTs) for local calibration of class probability estimates. To achieve calibration of a base learner, the PCT class must be used as the meta learner in the CascadeGeneralization class, which is also included in this package. The classifier to be calibrated must be used as the base learner in the CascadeGeneralization class. The CascadeGeneralization class can also be used independently to perform CascadeGeneralization for ensemble learning. The code for PCTs is largely the same as the LMT code for growing logistic model trees. For more details, see the ACML paper on probability calibration trees.

Group: nz.ac.waikato.cms.weka Artifact: probabilityCalibrationTrees
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Artifact probabilityCalibrationTrees
Group nz.ac.waikato.cms.weka
Version 1.0.0
Last update 26. November 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/probabilityCalibrationTrees
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

ensemblesOfNestedDichotomies from group nz.ac.waikato.cms.weka (version 1.0.6)

A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. In: PKDD, 84-95, 2005. Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for multi-class problems. In: Twenty-first International Conference on Machine Learning, 2004.

Group: nz.ac.waikato.cms.weka Artifact: ensemblesOfNestedDichotomies
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Artifact ensemblesOfNestedDichotomies
Group nz.ac.waikato.cms.weka
Version 1.0.6
Last update 21. February 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/ensemblesOfNestedDichotomies
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

LibSVM from group nz.ac.waikato.cms.weka (version 1.0.10)

A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier). LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool. LibSVM reports many useful statistics about LibSVM classifier (e.g., confusion matrix,precision, recall, ROC score, etc.)

Group: nz.ac.waikato.cms.weka Artifact: LibSVM
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Artifact LibSVM
Group nz.ac.waikato.cms.weka
Version 1.0.10
Last update 20. November 2016
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/LibSVM
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, libsvm,
There are maybe transitive dependencies!

complementNaiveBayes from group nz.ac.waikato.cms.weka (version 1.0.3)

Class for building and using a Complement class Naive Bayes classifier. For more information see: Jason D. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger: Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In: ICML, 616-623, 2003. P.S.: TF, IDF and length normalization transforms, as described in the paper, can be performed through weka.filters.unsupervised.StringToWordVector.

Group: nz.ac.waikato.cms.weka Artifact: complementNaiveBayes
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Artifact complementNaiveBayes
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 29. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/complementNaiveBayes
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

thresholdSelector from group nz.ac.waikato.cms.weka (version 1.0.3)

A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. The midpoint threshold is set so that a given performance measure is optimized. Currently this is the F-measure. Performance is measured either on the training data, a hold-out set or using cross-validation. In addition, the probabilities returned by the base learner can have their range expanded so that the output probabilities will reside between 0 and 1 (this is useful if the scheme normally produces probabilities in a very narrow range).

Group: nz.ac.waikato.cms.weka Artifact: thresholdSelector
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Artifact thresholdSelector
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 25. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/thresholdSelector
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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hyperPipes from group nz.ac.waikato.cms.weka (version 1.0.2)

Class implementing a HyperPipe classifier. For each category a HyperPipe is constructed that contains all points of that category (essentially records the attribute bounds observed for each category). Test instances are classified according to the category that "most contains the instance". Does not handle numeric class, or missing values in test cases. Extremely simple algorithm, but has the advantage of being extremely fast, and works quite well when you have "smegloads" of attributes.

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

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,
There are maybe transitive dependencies!

version-edit-plugin from group com.soerensen.maven.plugins (version 0.0.1)

This tool allows setting the version number of a specified dependency in a given Maven pom.xml file. All original comments and formatting will be kept. Usage example: mvn com.soerensen.maven.plugins:version-edit-plugin:1.0.1:setDependencyVersion -DpomFile="pom.xml" -Dgavtc="commons-io:commons-io:4.8.0" mvn com.soerensen.maven.plugins:version-edit-plugin:0.0.1:setPropertyDependencyVersion -DpropertyFile="test.property" -Dkey="junit_junit" -Dvalue="24.0.0" gavtc -> groupId:artifactId:version:type*:classifier* * optional Extra property: - noBackupFile (default:false), used to ignore the creation of a backup file.

Group: com.soerensen.maven.plugins Artifact: version-edit-plugin
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Artifact version-edit-plugin
Group com.soerensen.maven.plugins
Version 0.0.1
Last update 29. April 2019
Organization not specified
URL https://www.soerensen.com
License Apache License, Version 2.0
Dependencies amount 2
Dependencies maven-plugin-api, maven-plugin-annotations,
There are maybe transitive dependencies!

learning from group de.cit-ec.tcs.alignment (version 3.1.1)

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"

Group: de.cit-ec.tcs.alignment Artifact: learning
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Artifact learning
Group de.cit-ec.tcs.alignment
Version 3.1.1
Last update 26. October 2018
Organization not specified
URL http://openresearch.cit-ec.de/projects/tcs
License The GNU Affero General Public License, Version 3
Dependencies amount 1
Dependencies algorithms,
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ordinalClassClassifier from group nz.ac.waikato.cms.weka (version 1.0.5)

Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001. Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.

Group: nz.ac.waikato.cms.weka Artifact: ordinalClassClassifier
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Artifact ordinalClassClassifier
Group nz.ac.waikato.cms.weka
Version 1.0.5
Last update 06. December 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/ordinalClassClassifier
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

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