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interactive-bin-packing from group org.cicirello (version 3.1.5)

The Interactive Bin Packing Application provides a self-guided tutorial on combinatorial optimization, the bin packing problem, and constructive heuristics for bin packing. It also enables the user to interact with bin packing instances to explore their own problem solving strategies, or to test their knowledge of the various constructive heuristics covered by the tutorial. The application is not a solver for bin packing. The Interactive Bin Packing Application is a tool for learning about the bin packing problem, as well as for learning about heuristic techniques for solving instances of the problem.

Group: org.cicirello Artifact: interactive-bin-packing
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Artifact interactive-bin-packing
Group org.cicirello
Version 3.1.5
Last update 07. August 2023
Organization Cicirello.Org
URL https://github.com/cicirello/InteractiveBinPacking
License GPL-3.0-or-later
Dependencies amount 0
Dependencies No dependencies
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data-science from group org.odpi.egeria (version 3.15)

The Data Science Open Metadata Access Service (OMAS) provides APIs and events for tools and applications focused on building all types of analytics models such as predictive models and machine learning models. It provides the ability to define the purpose and requirements for a model, along with lineage and audit information relating to the development and validation process associated with the model.

Group: org.odpi.egeria Artifact: data-science
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Artifact data-science
Group org.odpi.egeria
Version 3.15
Last update 31. January 2023
Organization not specified
URL Not specified
License not specified
Dependencies amount 0
Dependencies No dependencies
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moa-kafka from group nz.ac.waikato.cms.adams (version 2022.10.17)

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. This artifact enables you to stream data to MOA from Kafka.

Group: nz.ac.waikato.cms.adams Artifact: moa-kafka
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Artifact moa-kafka
Group nz.ac.waikato.cms.adams
Version 2022.10.17
Last update 17. October 2022
Organization University of Waikato, Hamilton, NZ
URL http://moa.cms.waikato.ac.nz/
License GNU General Public License 3
Dependencies amount 2
Dependencies moa, kafka-clients,
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weka-package from group nz.ac.waikato.cms.adams (version 2022.10.17)

Group: nz.ac.waikato.cms.adams Artifact: weka-package
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Artifact weka-package
Group nz.ac.waikato.cms.adams
Version 2022.10.17
Last update 17. October 2022
Organization University of Waikato, Hamilton, NZ
URL http://moa.cms.waikato.ac.nz/
License GNU General Public License 3
Dependencies amount 1
Dependencies moa,
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moa from group nz.ac.waikato.cms.adams (version 2022.10.17)

Group: nz.ac.waikato.cms.adams Artifact: moa
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Artifact moa
Group nz.ac.waikato.cms.adams
Version 2022.10.17
Last update 17. October 2022
Organization University of Waikato, Hamilton, NZ
URL http://moa.cms.waikato.ac.nz/
License GNU General Public License 3.0
Dependencies amount 16
Dependencies weka-dev, sizeofag, guava, jclasslocator, jshell-scripting, meka, commons-math3, opencsv, commons-cli, commons-io, jfreechart, gson, ssj, JMathPlot, JMathIO, slf4j-log4j12,
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moa-pom from group nz.ac.waikato.cms.adams (version 2022.10.17)

Group: nz.ac.waikato.cms.adams Artifact: moa-pom

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Artifact moa-pom
Group nz.ac.waikato.cms.adams
Version 2022.10.17
Last update 17. October 2022
Organization University of Waikato, Hamilton, NZ
URL http://moa.cms.waikato.ac.nz/
License GNU General Public License 3.0
Dependencies amount 0
Dependencies No dependencies
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meka from group net.sf.meka (version 1.9.7)

The MEKA project provides an open source implementation of methods for multi-label classification and evaluation. It is based on the WEKA Machine Learning Toolkit. Several benchmark methods are also included, as well as the pruned sets and classifier chains methods, other methods from the scientific literature, and a wrapper to the MULAN framework.

Group: net.sf.meka Artifact: meka
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Artifact meka
Group net.sf.meka
Version 1.9.7
Last update 16. October 2022
Organization not specified
URL http://meka.sourceforge.net/
License GNU General Public License 3
Dependencies amount 13
Dependencies weka-dev, mulan, mst, flatlaf, jfilechooser-bookmarks, jclipboardhelper, jshell-scripting, jama, trove4j, bmad, autoencoder, markdownj-core, multisearch-weka-package,
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openmldb-nearline-tablet from group com.4paradigm.openmldb (version 0.3.2-macos)

Group: com.4paradigm.openmldb Artifact: openmldb-nearline-tablet
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Artifact openmldb-nearline-tablet
Group com.4paradigm.openmldb
Version 0.3.2-macos
Last update 18. November 2021
Organization not specified
URL Not specified
License not specified
Dependencies amount 10
Dependencies slf4j-api, commons-io, openmldb-common, slf4j-simple, brpc-java, protobuf-java, protobuf-java-util, hadoop-common, hadoop-hdfs, iceberg-core,
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self-tuning-lam-experiments from group org.cicirello (version 1.0.0)

This package contains Java programs for reproducing the experiments, and analysis of experimental data, from the following article: Vincent A. Cicirello. 2021. Self-Tuning Lam Annealing: Learning Hyperparameters While Problem Solving. Applied Sciences, 11, 21, Article 9828 (November 2021). https://doi.org/10.3390/app11219828. Also available at: https://www.cicirello.org/publications/applsci-11-09828.pdf

Group: org.cicirello Artifact: self-tuning-lam-experiments
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Artifact self-tuning-lam-experiments
Group org.cicirello
Version 1.0.0
Last update 21. October 2021
Organization Cicirello.Org
URL https://github.com/cicirello/self-tuning-lam-experiments
License GPL-3.0-or-later
Dependencies amount 1
Dependencies chips-n-salsa,
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LBFGS from group com.github.thssmonkey (version 1.0.4)

Limited-memory BFGS (L-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm using a limited amount of computer memory. It is a popular algorithm for parameter estimation in machine learning. The algorithm's target problem is to minimize f(x) over unconstrained values of the real-vector x where f is a differentiable scalar function.

Group: com.github.thssmonkey Artifact: LBFGS
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Artifact LBFGS
Group com.github.thssmonkey
Version 1.0.4
Last update 16. May 2019
Organization not specified
URL https://github.com/thssmonkey/LBFGS
License The Apache Software License, Version 2.0
Dependencies amount 4
Dependencies flink-scala_${scala.binary.version}, flink-streaming-scala_${scala.binary.version}, flink-clients_${scala.binary.version}, flink-ml_${scala.binary.version},
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