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Machine Learning

de.lmu.ifi.dbs.elki

ELKI - Batik Visualization

 de.lmu.ifi.dbs.elki : elki-batik-visualization

ELKI - Batik Visualization – Open-Source Data-Mining Framework with Index Acceleration

Last Version: 0.7.5

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Java Statistical Analysis Tool

 com.edwardraff : JSAT

A general purpose Machine Learning library.

Last Version: 0.0.9

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uk.gov.dstl.baleen

Baleen Mallet

 uk.gov.dstl.baleen : baleen-mallet

Structured information from unstructured data

Last Version: 2.7.0

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de.julielab

jcore-mallet-0.4

 de.julielab : jcore-mallet-0.4

The POM for the JCoRe Dependencies projects.

Last Version: 1.0.0

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MAchine Learning for LanguagE Toolkit (MALLET)

 com.github.rrodriguessilico : mallet

MALLET 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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org.jdmp

jdmp-mallet

 org.jdmp : jdmp-mallet

Plugin to incorporate text mining algorithms from Mallet

Last Version: 0.3.0

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ch.epfl.bbp.nlp

Mallet Utils

 ch.epfl.bbp.nlp : mallet_utils

Utilities for Mallet toolkit

Last Version: 1.0.1

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org.dkpro.tc

dkpro-tc-ml-mallet

 org.dkpro.tc : dkpro-tc-ml-mallet

Interface to the Mallet Machine Learning Toolkit

Last Version: 0.8.0

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optics_dbScan

 nz.ac.waikato.cms.weka : optics_dbScan

The 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

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XMeans

 nz.ac.waikato.cms.weka : XMeans

Cluster 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

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predictiveApriori

 nz.ac.waikato.cms.weka : predictiveApriori

Class 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

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prefuseGraph

 nz.ac.waikato.cms.weka : prefuseGraph

A 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

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normalize

 nz.ac.waikato.cms.weka : normalize

An instance filter that normalize instances considering only numeric attributes and ignoring class index

Last Version: 1.0.2

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SMOTE

 nz.ac.waikato.cms.weka : SMOTE

Resamples 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

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fastCorrBasedFS

 nz.ac.waikato.cms.weka : fastCorrBasedFS

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.

Last Version: 1.0.2

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decorate

 nz.ac.waikato.cms.weka : decorate

DECORATE 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

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prefuseTree

 nz.ac.waikato.cms.weka : prefuseTree

A 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

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partialLeastSquares

 nz.ac.waikato.cms.weka : partialLeastSquares

This 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

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discriminantAnalysis

 nz.ac.waikato.cms.weka : discriminantAnalysis

Currently only contains Fisher's Linear Discriminant Analysis.

Last Version: 1.0.3

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io.radicalbit

flink-jpmml-assets

 io.radicalbit : flink-jpmml-assets_2.10

Last Version: 0.6.1

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JPMML H2O.ai converter batch testing harness

 org.jpmml : pmml-h2o-testing

JPMML H2O.ai to class model converter batch testing harness

Last Version: 1.2.0

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org.jpmml

JPMML

 org.jpmml : jpmml

Java API for managing and evaluating models in Predictive Model Markup Language (PMML)

Last Version: 1.0.22

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io.radicalbit

flink-jpmml-scala

 io.radicalbit : flink-jpmml-scala_2.11

Last Version: 0.6.3

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JPMML XGBoost converter batch testing harness

 org.jpmml : pmml-xgboost-testing

JPMML XGBoost to class model converter batch testing harness

Last Version: 1.6.0

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io.radicalbit

flink-jpmml-assets

 io.radicalbit : flink-jpmml-assets_2.11

Last Version: 0.6.3

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io.radicalbit

flink-jpmml-scala

 io.radicalbit : flink-jpmml-scala_2.10

Last Version: 0.6.1

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JPMML LightGBM converter batch testing harness

 org.jpmml : pmml-lightgbm-testing

JPMML LightGBM to class model converter batch testing harness

Last Version: 1.4.0

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escher

 com.github.moaxcp.escher : escher

An x11 client written in java.

Last Version: 0.4.0

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chain3j

 io.github.moacchain : abi

null

Last Version: 0.1.0

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yido-evaluation

 org.moara.yido : evaluation

evaluation

Last Version: 0.1.0

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graphs-graphviz

 com.github.moaxcp.graphs : graphs-graphviz

supports converting graphs to dot format and images

Last Version: 0.14.0

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persian-date

 me.moallemi : persian-date

Persian Date and Persian Calendar for Java, Kotlin and Android

Last Version: 0.0.2

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yido-tokenizer

 org.moara.yido : tokenizer

korean tokenizer

Last Version: 0.1.2

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moar-concurrent

 com.github.rholder : moar-concurrent

This 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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com.yahoo.labs.samoa

samoa-instances

 com.yahoo.labs.samoa : samoa-instances

Instances for SAMOA

Last Version: 0.2.0

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samoa-instances

 org.apache.samoa : samoa-instances

Instances for SAMOA

Last Version: 0.5.0-incubating

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chain3j

 io.github.moacchain : codegen

null

Last Version: 0.1.0

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emagin-jfxcore-mobile-demoapp

 com.nexitia.emaginplatform : emagin-jfxcore-mobile-demoapp

Multiplatform JAVA Application

Last Version: 11.0.2

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chain3j

 io.github.moacchain : rlp

null

Last Version: 0.1.0

Release Date:
com.nexitia.emaginplatform

Emagin jfx core demoapp

 com.nexitia.emaginplatform : emagin-jfxcore-demoapp

Last Version: 11.0.3

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chain3j

 io.github.moacchain : tuples

null

Last Version: 0.1.0

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cosmos

 com.immomo.momomedia : momoaudio

project description

Last Version: 3.2.1.202112161420

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gif-builder

 com.github.moaxcp : gif-builder

gif builder

Last Version: 0.2.0

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chain3j

 io.github.moacchain : crypto

null

Last Version: 0.1.0

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yido-splitter

 org.moara.yido : splitter

The splitter what performs split by splitResult or paragraph unit.

Last Version: 0.1.0

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Datumbox Framework Core

 com.datumbox : datumbox-framework-core

Datumbox 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

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Datumbox Framework InMemory Storage Engine

 com.datumbox : datumbox-framework-storage-inmemory

Datumbox 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

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Datumbox Framework MapDB Storage Engine

 com.datumbox : datumbox-framework-storage-mapdb

Datumbox 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-applications

Datumbox 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

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konduit-serving-deeplearning4j-config

 ai.konduit.serving : konduit-serving-deeplearning4j-config

Enterprise runtime for machine learning models

Last Version: 0.0.2

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