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constraint-acquisition-platform from group fr.lirmm.cap (version 0.0.1b)

Group: fr.lirmm.cap Artifact: constraint-acquisition-platform
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Artifact constraint-acquisition-platform
Group fr.lirmm.cap
Version 0.0.1b


ConstraintAcquisition2 from group fr.lirmm.cap (version 0.0.1a)

Group: fr.lirmm.cap Artifact: ConstraintAcquisition2
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Artifact ConstraintAcquisition2
Group fr.lirmm.cap
Version 0.0.1a


multiInstanceLearning from group nz.ac.waikato.cms.weka (version 1.0.9)

Group: nz.ac.waikato.cms.weka Artifact: multiInstanceLearning
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prefuseTree from group nz.ac.waikato.cms.weka (version 1.0.3)

Group: nz.ac.waikato.cms.weka Artifact: prefuseTree
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Artifact prefuseTree
Group nz.ac.waikato.cms.weka
Version 1.0.3


teachingbox-core from group org.sf.teachingbox (version 1.2.3)

The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners.

Group: org.sf.teachingbox Artifact: teachingbox-core
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Download teachingbox-core.jar (1.2.3)
 

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Artifact teachingbox-core
Group org.sf.teachingbox
Version 1.2.3
Last update 08. November 2016
Organization TeachingBox
URL http://sourceforge.net/projects/teachingbox/
License GNU General Public License, Version 3
Dependencies amount 14
Dependencies guava, jfreechart, surfaceplotter, log4j, commons-lang3, commons-math3, colt, junit, thrift, concurrent, encog-core, weka-stable, nrjavaserial, redstone,
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denormalize from group nz.ac.waikato.cms.weka (version 1.0.3)

An instance filter that collapses instances with a common grouping ID value into a single instance. Useful for converting transactional data into a format that Weka's association rule learners can handle. IMPORTANT: assumes that the incoming batch of instances has been sorted on the grouping attribute. The values of nominal attributes are converted to indicator attributes. These can be either binary (with f and t values) or unary with missing values used to indicate absence. The later is Weka's old market basket format, which is useful for Apriori. Numeric attributes can be aggregated within groups by computing the average, sum, minimum or maximum.

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