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quickhull3d from group com.github.quickhull3d (version 1.0.0)

This is a 3D implementation of QuickHull for Java, based on the original paper by Barber, Dobkin, and Huhdanpaa and the C implementation known as qhull. The algorithm has O(n log(n)) complexity, works with double precision numbers, is fairly robust with respect to degenerate situations, and allows the merging of co-planar faces.

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Artifact quickhull3d
Group com.github.quickhull3d
Version 1.0.0
Last update 31. October 2014
Organization John E. Lloyd
URL http://quickhull3d.github.io/quickhull3d
License GNU LESSER GENERAL PUBLIC LICENSE (LGPL) version 3
Dependencies amount 1
Dependencies slf4j-api,
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learnlib-rpni 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. More details on the blue-fringe version of 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
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Artifact learnlib-rpni
Group de.learnlib
Version 0.17.0
Last update 15. November 2023
Organization not specified
URL Not specified
License not specified
Dependencies amount 8
Dependencies learnlib-api, learnlib-datastructure-pta, automata-api, automata-commons-smartcollections, checker-qual, buildergen, learnlib-learner-it-support, testng,
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automata-modelchecking-ltsmin from group net.automatalib (version 0.11.0)

This artifact provides the integration of the model checker "LTSmin" (https://ltsmin.utwente.nl/) as described in the paper "Sound Black-Box Checking in the LearnLib" (https://dx.doi.org/10.1007/s11334-019-00342-6) by Jeroen Meijer and Jaco van de Pol. Note that this implementation requires a local installation of the LTSmin binaries (see https://ltsmin.utwente.nl/) which are not explicitly included in this artifact due to packaging reasons.

Group: net.automatalib Artifact: automata-modelchecking-ltsmin
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Artifact automata-modelchecking-ltsmin
Group net.automatalib
Version 0.11.0
Last update 06. November 2023
Organization not specified
URL Not specified
License not specified
Dependencies amount 11
Dependencies automata-api, automata-commons-util, automata-core, automata-serialization-etf, automata-serialization-fsm, automata-util, guava, slf4j-api, buildergen, checker-qual, testng,
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IAMsystem from group fr.erias (version 2.2.0)

A fast dictionary-based approach for semantic annotation / entity linking. IAMsystem is efficient at annotating documents with large dictionaries and approximate string matching algorithms. Description of the system can be found in the following paper : "Cossin S, Jouhet V, Mougin F, Diallo G and Thiessard F. IAM at CLEF eHealth 2018: Concept Annotation and Coding in French Death Certificates. CEUR-WS. September 2018"

Group: fr.erias Artifact: IAMsystem
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Artifact IAMsystem
Group fr.erias
Version 2.2.0
Last update 31. March 2023
Organization Computer research applied to health ERIAS - Bordeaux Population Health - Research Center U1219
URL https://github.com/scossin/IAMsystem
License MIT License
Dependencies amount 2
Dependencies liblevenshtein, org.apache.commons.codec,
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jcore-mutationfinder-ae from group de.julielab (version 2.6.1)

An analysis engine to recognize mentions of gene point mutations in document text. This is a wrapper around the original MutationFinder (http://mutationfinder.sourceforge.net/), published in the following paper: MutationFinder: A high-performance system for extracting point mutation mentions from text J. Gregory Caporaso, William A. Baumgartner Jr., David A. Randolph, K. Bretonnel Cohen, and Lawrence Hunter; Bioinformatics, 2007 23(14):1862-1865; doi:10.1093/bioinformatics/btm235;

Group: de.julielab Artifact: jcore-mutationfinder-ae
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Artifact jcore-mutationfinder-ae
Group de.julielab
Version 2.6.1
Last update 18. December 2022
Organization not specified
URL Not specified
License BSD-2-Clause
Dependencies amount 6
Dependencies oro, junit-jupiter-engine, jcore-types, jcore-descriptor-creator, slf4j-api, assertj-core,
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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,
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carti-model from group be.uantwerpen.adrem (version 0.1)

This package provides data model and the base classes for the cartification. More info about cartification can be found in the paper "Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data" by Emin Aksehirli, Bart Goethals, Emmanuel Müller and Jilles Vreeken https://dx.doi.org/10.1109/ICDM.2013.146

Group: be.uantwerpen.adrem Artifact: carti-model
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Artifact carti-model
Group be.uantwerpen.adrem
Version 0.1
Last update 23. September 2015
Organization University of Antwerp - ADReM Research Group
URL http://maven.apache.org
License GNU AFFERO GENERAL PUBLIC LICENSE, Version 3
Dependencies amount 1
Dependencies fim-model,
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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!

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,
There are maybe transitive dependencies!

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