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ensembleLibrary from group nz.ac.waikato.cms.weka (version 1.0.4)

Manages a libary of ensemble classifiers

Group: nz.ac.waikato.cms.weka Artifact: ensembleLibrary

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Artifact ensembleLibrary
Group nz.ac.waikato.cms.weka
Version 1.0.4

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

treetable from group net.sf.squirrel-sql.thirdparty-non-maven (version 20040121)

Example code for creating JTables with an embedded tree structure in Swing.

Group: net.sf.squirrel-sql.thirdparty-non-maven Artifact: treetable

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Artifact treetable
Group net.sf.squirrel-sql.thirdparty-non-maven
Version 20040121

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

Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.

Group: nz.ac.waikato.cms.weka Artifact: chiSquaredAttributeEval

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Artifact chiSquaredAttributeEval
Group nz.ac.waikato.cms.weka
Version 1.0.2

camelcase-always from group org.andromda.thirdparty.jaxb2_commons (version 1.0)

Group: org.andromda.thirdparty.jaxb2_commons Artifact: camelcase-always

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Artifact camelcase-always
Group org.andromda.thirdparty.jaxb2_commons
Version 1.0

collection-setter-injector from group net.java.dev.vcc.thirdparty (version 0.5.0-1)

Group: net.java.dev.vcc.thirdparty Artifact: collection-setter-injector

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Artifact collection-setter-injector
Group net.java.dev.vcc.thirdparty
Version 0.5.0-1

xmldb-api from group org.exist-db.thirdparty.org.xmldb (version 20040731)

XML:DB API patched for eXist-db

Group: org.exist-db.thirdparty.org.xmldb Artifact: xmldb-api

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Artifact xmldb-api
Group org.exist-db.thirdparty.org.xmldb
Version 20040731

JRI from group org.nuiton.thirdparty (version 0.9-6)

Group: org.nuiton.thirdparty Artifact: JRI

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Artifact JRI
Group org.nuiton.thirdparty
Version 0.9-6

fcms-widgets from group com.github.waikato (version 0.0.7)

Small collection of useful Java widgets.

Group: com.github.waikato Artifact: fcms-widgets

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Artifact fcms-widgets
Group com.github.waikato
Version 0.0.7

org-openide-util from group uk.gov.nationalarchives.thirdparty.netbeans (version 7.2)

NetBeans OpenIDE utilities

Group: uk.gov.nationalarchives.thirdparty.netbeans Artifact: org-openide-util
Show documentation Show source

1 downloads

Artifact org-openide-util
Group uk.gov.nationalarchives.thirdparty.netbeans
Version 7.2

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

This package provides four search methods for attribute selection: ExhaustiveSearch, GeneticSearch, RandomSearch and RankSearch. See: David E. Goldberg (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley. Mark Hall, Geoffrey Holmes (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering. 15(6):1437-1447.

Group: nz.ac.waikato.cms.weka Artifact: attributeSelectionSearchMethods

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Artifact attributeSelectionSearchMethods
Group nz.ac.waikato.cms.weka
Version 1.0.2

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)