JarCastingMachine Learning
ELKI - Batik Visualization
de.lmu.ifi.dbs.elki : elki-batik-visualizationELKI - Batik Visualization – Open-Source Data-Mining Framework with Index Acceleration
Last Version: 0.7.5
Release Date:Last Version: 0.0.9
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Last Version: 2.7.0
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Last Version: 1.0.0
Release Date:MAchine Learning for LanguagE Toolkit (MALLET)
com.github.rrodriguessilico : malletMALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
Last Version: 2.0.8-RC3-Unofficial
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Last Version: 0.3.0
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Last Version: 1.0.1
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dkpro-tc-ml-mallet
org.dkpro.tc : dkpro-tc-ml-malletInterface to the Mallet Machine Learning Toolkit
Last Version: 0.8.0
Release Date:optics_dbScan
nz.ac.waikato.cms.weka : optics_dbScanThe OPTICS and DBScan clustering algorithms. Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Second International Conference on Knowledge Discovery and Data Mining, 226-231, 1996; Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure. In: ACM SIGMOD International Conference on Management of Data, 49-60, 1999.
Last Version: 1.0.6
Release Date:XMeans
nz.ac.waikato.cms.weka : XMeansCluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures. For more information see: Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000.
Last Version: 1.0.6
Release Date:predictiveApriori
nz.ac.waikato.cms.weka : predictiveAprioriClass implementing the predictive apriori algorithm for mining association rules. It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value. For more information see: Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. In: 5th European Conference on Principles of Data Mining and Knowledge Discovery, 424-435, 2001.
Last Version: 1.0.4
Release Date:prefuseGraph
nz.ac.waikato.cms.weka : prefuseGraphA visualization component for displaying tree structures from those schemes that can output graphs (e.g. bayes nets). This component is available from the popup menu in the Explorer's classify. The component uses the prefuse visualization library.
Last Version: 1.0.4
Release Date:normalize
nz.ac.waikato.cms.weka : normalizeAn instance filter that normalize instances considering only numeric attributes and ignoring class index
Last Version: 1.0.2
Release Date:SMOTE
nz.ac.waikato.cms.weka : SMOTEResamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE). The original dataset must fit entirely in memory. The amount of SMOTE and number of nearest neighbors may be specified. For more information, see Nitesh V. Chawla et. al. (2002). Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research. 16:321-357.
Last Version: 1.0.3
Release Date:fastCorrBasedFS
nz.ac.waikato.cms.weka : fastCorrBasedFSFeature 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.
Last Version: 1.0.2
Release Date:decorate
nz.ac.waikato.cms.weka : decorateDECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests. Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets. For more details see: P. Melville, R. J. Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples. In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003; P. Melville, R. J. Mooney (2004). Creating Diversity in Ensembles Using Artificial Data. Information Fusion: Special Issue on Diversity in Multiclassifier Systems.
Last Version: 1.0.3
Release Date:prefuseTree
nz.ac.waikato.cms.weka : prefuseTreeA visualization component for displaying tree structures from those schemes that can output trees (e.g. decision tree learners, Cobweb clusterer etc.). This component is available from the popup menu in the Explorer's classify and cluster panels. The component uses the prefuse visualization library.
Last Version: 1.0.3
Release Date:partialLeastSquares
nz.ac.waikato.cms.weka : partialLeastSquaresThis package contains a filter for computing partial least squares and transforming the input data into the PLS space. It also contains a classifier for performing PLS regression.
Last Version: 1.0.5
Release Date:discriminantAnalysis
nz.ac.waikato.cms.weka : discriminantAnalysisCurrently only contains Fisher's Linear Discriminant Analysis.
Last Version: 1.0.3
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Last Version: 0.6.1
Release Date:JPMML H2O.ai converter batch testing harness
org.jpmml : pmml-h2o-testingJPMML H2O.ai to class model converter batch testing harness
Last Version: 1.2.0
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Last Version: 0.6.3
Release Date:JPMML XGBoost converter batch testing harness
org.jpmml : pmml-xgboost-testingJPMML XGBoost to class model converter batch testing harness
Last Version: 1.6.0
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Last Version: 0.6.3
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Last Version: 0.6.1
Release Date:JPMML LightGBM converter batch testing harness
org.jpmml : pmml-lightgbm-testingJPMML LightGBM to class model converter batch testing harness
Last Version: 1.4.0
Release Date:Last Version: 0.4.0
Release Date:Last Version: 0.1.0
Release Date:Last Version: 0.1.0
Release Date:graphs-graphviz
com.github.moaxcp.graphs : graphs-graphvizsupports converting graphs to dot format and images
Last Version: 0.14.0
Release Date:persian-date
me.moallemi : persian-datePersian Date and Persian Calendar for Java, Kotlin and Android
Last Version: 0.0.2
Release Date:Last Version: 0.1.2
Release Date:moar-concurrent
com.github.rholder : moar-concurrentThis module contains a collection of useful builders and concurrency classes to assist in modeling complex or overly tweakable concurrent processing pipelines.
Last Version: 1.0.3
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Last Version: 0.2.0
Release Date:Last Version: 0.5.0-incubating
Release Date:Last Version: 0.1.0
Release Date:emagin-jfxcore-mobile-demoapp
com.nexitia.emaginplatform : emagin-jfxcore-mobile-demoappMultiplatform JAVA Application
Last Version: 11.0.2
Release Date:Last Version: 0.1.0
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Last Version: 11.0.3
Release Date:Last Version: 0.1.0
Release Date:Last Version: 3.2.1.202112161420
Release Date:Last Version: 0.2.0
Release Date:Last Version: 0.1.0
Release Date:yido-splitter
org.moara.yido : splitterThe splitter what performs split by splitResult or paragraph unit.
Last Version: 0.1.0
Release Date:Datumbox Framework Core
com.datumbox : datumbox-framework-coreDatumbox is an open-source Machine Learning Framework written in Java which allows the rapid development of Machine Learning and Statistical applications.
Last Version: 0.8.2
Release Date:Datumbox Framework InMemory Storage Engine
com.datumbox : datumbox-framework-storage-inmemoryDatumbox is an open-source Machine Learning Framework written in Java which allows the rapid development of Machine Learning and Statistical applications.
Last Version: 0.8.2
Release Date:Datumbox Framework MapDB Storage Engine
com.datumbox : datumbox-framework-storage-mapdbDatumbox is an open-source Machine Learning Framework written in Java which allows the rapid development of Machine Learning and Statistical applications.
Last Version: 0.8.2
Release Date:Datumbox Framework Applications
com.datumbox : datumbox-framework-applicationsDatumbox is an open-source Machine Learning Framework written in Java which allows the rapid development of Machine Learning and Statistical applications.
Last Version: 0.8.2
Release Date:konduit-serving-deeplearning4j-config
ai.konduit.serving : konduit-serving-deeplearning4j-configEnterprise runtime for machine learning models
Last Version: 0.0.2
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