Прикладные библиотеки

dl4j-spark

org.deeplearning4j : dl4j-spark

DeepLearning for java

Последняя версия: 0.4-rc3.9

Дата: 17 мая 2016 г.

nn-dl4j

com.linkedin.dagli : nn-dl4j

DAG-oriented machine learning framework for bug-resistant, readable, efficient, maintainable and trivially deployable models in Java and other JVM languages

Последняя версия: 15.0.0-beta9

Дата: 5 окт. 2021 г.

dl4j-spark

org.deeplearning4j : dl4j-spark3_2.12

DeepLearning for java

Последняя версия: 1.0.0-M2

Дата: 25 февр. 2022 г.

wsdl4j

org.wso2.wsdl4j : wsdl4j

WSDL4J Parent.

Последняя версия: 1.6.3.wso2v3

Дата: 10 янв. 2018 г.

ELKI - Parallel Processing Core

de.lmu.ifi.dbs.elki : elki-core-parallel

ELKI - Parallel Processing Core – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Itemset Mining

de.lmu.ifi.dbs.elki : elki-itemsets

ELKI - Itemset Mining – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - MiniGUI Command Line Builder

de.lmu.ifi.dbs.elki : elki-gui-minigui

ELKI - MiniGUI Command Line Builder – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Data Generator

de.lmu.ifi.dbs.elki : elki-data-generator

ELKI - Data Generator – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Core Distance Functions

de.lmu.ifi.dbs.elki : elki-core-distance

ELKI - Core Distance Functions – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Logging

de.lmu.ifi.dbs.elki : elki-logging

ELKI - Logging – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Geography Module

de.lmu.ifi.dbs.elki : elki-geo

ELKI - Geography Module – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Data Input

de.lmu.ifi.dbs.elki : elki-input

ELKI - Data Input – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - R-Tree Variants

de.lmu.ifi.dbs.elki : elki-index-rtree

ELKI - R-Tree Variants – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Various indexes

de.lmu.ifi.dbs.elki : elki-index-various

ELKI - Various indexes – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - External Distances

de.lmu.ifi.dbs.elki : elki-precomputed

ELKI - External Distances – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Locality Sensitive Hashing

de.lmu.ifi.dbs.elki : elki-index-lsh

ELKI - Locality Sensitive Hashing – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Classification Algorithms

de.lmu.ifi.dbs.elki : elki-classification

ELKI - Classification Algorithms – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Time Series

de.lmu.ifi.dbs.elki : elki-timeseries

ELKI - Time Series – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - M-Tree Variants

de.lmu.ifi.dbs.elki : elki-index-mtree

ELKI - M-Tree Variants – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Uncertain data handling

de.lmu.ifi.dbs.elki : elki-uncertain

ELKI - Uncertain data handling – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Core Data Types

de.lmu.ifi.dbs.elki : elki-core-data

ELKI - Core Data Types – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

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.