Machine Learning | JarCasting

ELKI - Batik Visualization

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

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

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

Java Statistical Analysis Tool

com.edwardraff : JSAT

A general purpose Machine Learning library.

Последняя версия: 0.0.9

Дата: 4 дек. 2017 г.

Baleen Mallet

uk.gov.dstl.baleen : baleen-mallet

Structured information from unstructured data

Последняя версия: 2.7.0

Дата: 8 мая 2019 г.

jcore-mallet-0.4

de.julielab : jcore-mallet-0.4

The POM for the JCoRe Dependencies projects.

Последняя версия: 1.0.0

Дата: 13 янв. 2019 г.

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.

Последняя версия: 2.0.8-RC3-Unofficial

Дата: 1 апр. 2016 г.

jdmp-mallet

org.jdmp : jdmp-mallet

Plugin to incorporate text mining algorithms from Mallet

Последняя версия: 0.3.0

Дата: 16 авг. 2015 г.

Mallet Utils

ch.epfl.bbp.nlp : mallet_utils

Utilities for Mallet toolkit

Последняя версия: 1.0.1

Дата: 11 мая 2015 г.

dkpro-tc-ml-mallet

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

Interface to the Mallet Machine Learning Toolkit

Последняя версия: 0.8.0

Дата: 21 апр. 2016 г.

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.

Последняя версия: 1.0.6

Дата: 16 дек. 2019 г.

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.

Последняя версия: 1.0.6

Дата: 28 февр. 2018 г.

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.

Последняя версия: 1.0.4

Дата: 5 авг. 2014 г.

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.

Последняя версия: 1.0.4

Дата: 17 сент. 2016 г.

normalize

nz.ac.waikato.cms.weka : normalize

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

Последняя версия: 1.0.2

Дата: 26 апр. 2012 г.

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.

Последняя версия: 1.0.3

Дата: 3 апр. 2013 г.

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.

Последняя версия: 1.0.2

Дата: 26 апр. 2012 г.

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.

Последняя версия: 1.0.3

Дата: 26 апр. 2012 г.

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.

Последняя версия: 1.0.3

Дата: 26 апр. 2012 г.

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.

Последняя версия: 1.0.5

Дата: 11 янв. 2018 г.

discriminantAnalysis

nz.ac.waikato.cms.weka : discriminantAnalysis

Currently only contains Fisher's Linear Discriminant Analysis.

Последняя версия: 1.0.3

Дата: 28 окт. 2018 г.

flink-jpmml-assets

io.radicalbit : flink-jpmml-assets_2.10

Последняя версия: 0.6.1

Дата: 27 сент. 2017 г.

JPMML H2O.ai converter batch testing harness

org.jpmml : pmml-h2o-testing

JPMML H2O.ai to class model converter batch testing harness

Последняя версия: 1.2.0

Дата: 16 дек. 2021 г.

JPMML

org.jpmml : jpmml

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

Последняя версия: 1.0.22

Дата: 17 февр. 2014 г.

flink-jpmml-scala

io.radicalbit : flink-jpmml-scala_2.11

Последняя версия: 0.6.3

Дата: 21 нояб. 2018 г.

JPMML XGBoost converter batch testing harness

org.jpmml : pmml-xgboost-testing

JPMML XGBoost to class model converter batch testing harness

Последняя версия: 1.6.0

Дата: 15 дек. 2021 г.

flink-jpmml-assets

io.radicalbit : flink-jpmml-assets_2.11

Последняя версия: 0.6.3

Дата: 21 нояб. 2018 г.

flink-jpmml-scala

io.radicalbit : flink-jpmml-scala_2.10

Последняя версия: 0.6.1

Дата: 27 сент. 2017 г.

JPMML LightGBM converter batch testing harness

org.jpmml : pmml-lightgbm-testing

JPMML LightGBM to class model converter batch testing harness

Последняя версия: 1.4.0

Дата: 15 дек. 2021 г.

escher

com.github.moaxcp.escher : escher

An x11 client written in java.

Последняя версия: 0.4.0

Дата: 25 апр. 2020 г.

chain3j

io.github.moacchain : abi

null

Последняя версия: 0.1.0

Дата: 2 янв. 2019 г.

yido-evaluation

org.moara.yido : evaluation

evaluation

Последняя версия: 0.1.0

Дата: 8 мар. 2021 г.

graphs-graphviz

com.github.moaxcp.graphs : graphs-graphviz

supports converting graphs to dot format and images

Последняя версия: 0.14.0

Дата: 13 июн. 2022 г.

persian-date

me.moallemi : persian-date

Persian Date and Persian Calendar for Java, Kotlin and Android

Последняя версия: 0.0.2

Дата: 25 апр. 2020 г.

yido-tokenizer

org.moara.yido : tokenizer

korean tokenizer

Последняя версия: 0.1.2

Дата: 4 дек. 2020 г.

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.

Последняя версия: 1.0.3

Дата: 3 февр. 2015 г.

samoa-instances

com.yahoo.labs.samoa : samoa-instances

Instances for SAMOA

Последняя версия: 0.2.0

Дата: 23 сент. 2014 г.

samoa-instances

org.apache.samoa : samoa-instances

Instances for SAMOA

Последняя версия: 0.5.0-incubating

Дата: 25 февр. 2020 г.

chain3j

io.github.moacchain : codegen

null

Последняя версия: 0.1.0

Дата: 2 янв. 2019 г.

emagin-jfxcore-mobile-demoapp

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

Multiplatform JAVA Application

Последняя версия: 11.0.2

Дата: 9 янв. 2019 г.

chain3j

io.github.moacchain : rlp

null

Последняя версия: 0.1.0

Дата: 2 янв. 2019 г.

Emagin jfx core demoapp

com.nexitia.emaginplatform : emagin-jfxcore-demoapp

Последняя версия: 11.0.3

Дата: 22 янв. 2019 г.

chain3j

io.github.moacchain : tuples

null

Последняя версия: 0.1.0

Дата: 2 янв. 2019 г.

cosmos

com.immomo.momomedia : momoaudio

project description

Последняя версия: 3.2.1.202112161420

Дата: 16 дек. 2021 г.

gif-builder

com.github.moaxcp : gif-builder

gif builder

Последняя версия: 0.2.0

Дата: 26 мая 2022 г.

chain3j

io.github.moacchain : crypto

null

Последняя версия: 0.1.0

Дата: 2 янв. 2019 г.

yido-splitter

org.moara.yido : splitter

The splitter what performs split by splitResult or paragraph unit.

Последняя версия: 0.1.0

Дата: 9 мар. 2021 г.

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.

Последняя версия: 0.8.2

Дата: 5 авг. 2020 г.

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.

Последняя версия: 0.8.2

Дата: 5 авг. 2020 г.

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.

Последняя версия: 0.8.2

Дата: 5 авг. 2020 г.

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.

Последняя версия: 0.8.2

Дата: 5 авг. 2020 г.

konduit-serving-deeplearning4j-config

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

Enterprise runtime for machine learning models

Последняя версия: 0.0.2

Дата: 26 февр. 2022 г.