Download JAR files tagged by boosting with all dependencies

nazgul-core-cache-impl-hazelcast from group se.jguru.nazgul.core.cache.impl.hazelcast (version 1.6.0)

Group: se.jguru.nazgul.core.cache.impl.hazelcast Artifact: nazgul-core-cache-impl-hazelcast
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Artifact nazgul-core-cache-impl-hazelcast
Group se.jguru.nazgul.core.cache.impl.hazelcast
Version 1.6.0
Last update 15. October 2013
Organization not specified
URL ${url.prefix}/${path.in.reactor}
License not specified
Dependencies amount 5
Dependencies nazgul-core-cache-api, hazelcast, hazelcast-client, commons-lang3, slf4j-api,
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nazgul-core-analyzer-impl-nazgul from group se.jguru.nazgul.core.quickstart.analyzer.impl.nazgul (version 1.6.0)

Group: se.jguru.nazgul.core.quickstart.analyzer.impl.nazgul Artifact: nazgul-core-analyzer-impl-nazgul
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Artifact nazgul-core-analyzer-impl-nazgul
Group se.jguru.nazgul.core.quickstart.analyzer.impl.nazgul
Version 1.6.0
Last update 15. October 2013
Organization not specified
URL ${url.prefix}/${path.in.reactor}
License not specified
Dependencies amount 1
Dependencies nazgul-core-analyzer-api,
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nazgul-core-analyzer-api from group se.jguru.nazgul.core.quickstart.analyzer.api (version 1.6.0)

Group: se.jguru.nazgul.core.quickstart.analyzer.api Artifact: nazgul-core-analyzer-api
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Artifact nazgul-core-analyzer-api
Group se.jguru.nazgul.core.quickstart.analyzer.api
Version 1.6.0
Last update 15. October 2013
Organization not specified
URL ${url.prefix}/${path.in.reactor}
License not specified
Dependencies amount 1
Dependencies commons-lang3,
There are maybe transitive dependencies!

nazgul-core-tree-model from group se.jguru.nazgul.core.algorithms.tree.model (version 1.5.1)

Group: se.jguru.nazgul.core.algorithms.tree.model Artifact: nazgul-core-tree-model
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Artifact nazgul-core-tree-model
Group se.jguru.nazgul.core.algorithms.tree.model
Version 1.5.1
Last update 31. July 2013
Organization not specified
URL ${url.prefix}/${path.in.reactor}
License not specified
Dependencies amount 4
Dependencies nazgul-core-persistence-model, commons-lang3, slf4j-api, jaxb-api-osgi,
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nazgul-core-model-parent from group se.jguru.nazgul.core.poms.model-parent (version 1.4.3)

Group: se.jguru.nazgul.core.poms.model-parent Artifact: nazgul-core-model-parent

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Artifact nazgul-core-model-parent
Group se.jguru.nazgul.core.poms.model-parent
Version 1.4.3
Last update 28. May 2013
Organization not specified
URL Not specified
License not specified
Dependencies amount 2
Dependencies nazgul-core-reflection-api, nazgul-core-xmlbinding-api,
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realAdaBoost from group nz.ac.waikato.cms.weka (version 1.0.2)

Class for boosting a 2-class classifier using the Real Adaboost method. For more information, see J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.

Group: nz.ac.waikato.cms.weka Artifact: realAdaBoost
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1 downloads

Artifact realAdaBoost
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/realAdaBoost
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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api-link from group io.github.hisondev (version 1.0.0)

API-Link is a novel solution for Spring projects, aimed at streamlining development by eliminating the need for individual controllers. It allows developers to use a single 'cmd' value to invoke service layer methods, simplifying workflow and boosting productivity.

Group: io.github.hisondev Artifact: api-link
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Artifact api-link
Group io.github.hisondev
Version 1.0.0
Last update 07. January 2024
Organization not specified
URL https://github.com/hisondev/api-link
License MIT License
Dependencies amount 2
Dependencies spring-boot-starter-websocket, data-model,
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mlrules-weka-package from group com.github.fracpete (version 2023.7.26)

Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy. Fork of the original code located at: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html

Group: com.github.fracpete Artifact: mlrules-weka-package
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Artifact mlrules-weka-package
Group com.github.fracpete
Version 2023.7.26
Last update 25. July 2023
Organization University of Waikato, Hamilton, NZ
URL https://github.com/fracpete/mlrules-weka-package
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

ordinalClassClassifier from group nz.ac.waikato.cms.weka (version 1.0.5)

Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001. Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.

Group: nz.ac.waikato.cms.weka Artifact: ordinalClassClassifier
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1 downloads

Artifact ordinalClassClassifier
Group nz.ac.waikato.cms.weka
Version 1.0.5
Last update 06. December 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/ordinalClassClassifier
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

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

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used. For more information, see: Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.

Group: nz.ac.waikato.cms.weka Artifact: bestFirstTree
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1 downloads

Artifact bestFirstTree
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 27. April 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/bestFirstTree
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

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