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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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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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jclal from group net.sf.jclal (version 1.1)

JCLAL is a software system for Active Learning research, developed in the Java programming language. It provides a high-level software framework and a robust object-oriented design that allows an easy use, extention, modification and reusability. It includes the most relevant query strategies proposed on the single-label and multi-label learning contexts.

Group: net.sf.jclal Artifact: jclal
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Artifact jclal
Group net.sf.jclal
Version 1.1
Last update 01. April 2016
Organization University of Holguín, Cuba- University of Cordoba, Spain
URL http://jclal.sf.net
License GNU General Public License 3
Dependencies amount 21
Dependencies commons-beanutils, commons-beanutils-core, commons-collections, commons-configuration, commons-digester, commons-lang, commons-logging, epsgraphics, hamcrest-core, java-cup, jcommon, jfreechart, jfreesvg, lucene-core, mail, moa, mulan, multiInstanceFilters, multiInstanceLearning, pentaho-package-manager, weka-dev,
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sfac from group net.sf.sfac (version 0.8)

This project is a simple Java-Swing framework giving strategies to implements usual graphical application concern (action management, application state, internationalization ...) The framework is easy to learn and use and is targeted for small Swing applications, it can also be a good start for learning swing. In addition, this project proposes useful Swing components like an advanced layout manager, a multi-column sorting table, object editor...

Group: net.sf.sfac Artifact: sfac
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Artifact sfac
Group net.sf.sfac
Version 0.8
Last update 13. March 2016
Organization not specified
URL http://sfac.sourceforge.net
License Apache 2
Dependencies amount 0
Dependencies No dependencies
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attributeSelectionSearchMethods from group nz.ac.waikato.cms.weka (version 1.0.7)

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 1.0.7
Last update 27. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/attributeSelectionSearchMethods
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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alternatingDecisionTrees from group nz.ac.waikato.cms.weka (version 1.0.5)

Binary-class and multi-class alternating decision trees. For more information see: Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999. Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.

Group: nz.ac.waikato.cms.weka Artifact: alternatingDecisionTrees
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Artifact alternatingDecisionTrees
Group nz.ac.waikato.cms.weka
Version 1.0.5
Last update 27. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/alternatingDecisionTrees
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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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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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,
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fastCorrBasedFS from group nz.ac.waikato.cms.weka (version 1.0.2)

Feature selection method based on correlation measureand relevance and redundancy analysis. Use in conjunction with an attribute set evaluator (SymmetricalUncertAttributeEval). For more information see: Lei Yu, Huan Liu: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In: Proceedings of the Twentieth International Conference on Machine Learning, 856-863, 2003.

Group: nz.ac.waikato.cms.weka Artifact: fastCorrBasedFS
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Artifact fastCorrBasedFS
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/fastCorrBasedFS
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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learnlib-rpni-mdl from group de.learnlib (version 0.17.0)

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "minimum description length" (MDL) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

Group: de.learnlib Artifact: learnlib-rpni-mdl
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Artifact learnlib-rpni-mdl
Group de.learnlib
Version 0.17.0
Last update 15. November 2023
Organization not specified
URL Not specified
License not specified
Dependencies amount 10
Dependencies learnlib-api, learnlib-datastructure-pta, learnlib-rpni, automata-api, automata-commons-smartcollections, automata-commons-util, automata-core, buildergen, learnlib-learner-it-support, testng,
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learnlib-rpni-edsm from group de.learnlib (version 0.17.0)

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "evidence-driven state merging" (EDSM) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

Group: de.learnlib Artifact: learnlib-rpni-edsm
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Artifact learnlib-rpni-edsm
Group de.learnlib
Version 0.17.0
Last update 15. November 2023
Organization not specified
URL Not specified
License not specified
Dependencies amount 11
Dependencies learnlib-api, learnlib-datastructure-pta, learnlib-rpni, guava, automata-api, automata-commons-smartcollections, automata-commons-util, automata-core, buildergen, learnlib-learner-it-support, testng,
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