Прикладные библиотеки

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

ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance and scalability, ELKI offers many data index structures such as the R*-tree that can provide major performance gains. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions in particular of new methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms.

Последняя версия: 0.7.1

Дата: 11 февр. 2016 г.

com.nelkinda.japi:nelkinda-japi-swing-parent

com.nelkinda.japi : nelkinda-japi-swing-parent

Make life with Swing more joyful.

Последняя версия: 0.0.2

Дата: 23 дек. 2016 г.

ELKI - 3D Parallel Coordinate Trees Visualization

de.lmu.ifi.dbs.elki : elki-3dpc

ELKI - 3D Parallel Coordinate Trees Visualization – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

nelkinda-japi-swing-test

com.nelkinda.japi : nelkinda-japi-swing-test

Make life with Swing more joyful.

Последняя версия: 0.0.2

Дата: 23 дек. 2016 г.

com.nelkinda.japi:nelkinda-japi-swing-examples-parent

com.nelkinda.japi : nelkinda-japi-swing-examples-parent

Make life with Swing more joyful.

Последняя версия: 0.0.2

Дата: 23 дек. 2016 г.

ELKI Data Mining Framework - Single-jar Bundle

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

ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance and scalability, ELKI offers many data index structures such as the R*-tree that can provide major performance gains. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions in particular of new methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms.

Последняя версия: 0.7.1

Дата: 11 февр. 2016 г.

ELKI - LibSVM based extensions

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

ELKI - LibSVM based extensions – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

ELKI - Documentation Utilities

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

ELKI - Documentation Utilities – Open-Source Data-Mining Framework with Index Acceleration

Последняя версия: 0.7.5

Дата: 15 февр. 2019 г.

commercetools-sdk-java-ml

com.commercetools.sdk : commercetools-sdk-java-ml

The e-commerce SDK from commercetools Composable Commerce for Java

Последняя версия: 8.10.0

Дата: 21 июн. 2022 г.

shijingsh-ai-jsat

com.github.shijingsh : shijingsh-ai-jsat

Sonatype helps open source projects to set up Maven repositories on https://oss.sonatype.org/

Последняя версия: 1.0.0

Дата: 2 апр. 2021 г.

jsatbuilder

hu.webarticum : jsatbuilder

SAT formula builder with constraint dependency tracking, written in Java

Последняя версия: 0.2

Дата: 25 авг. 2018 г.

mahout

io.github.qsy7.java.dependencies : apache-mahout

dependencies

Последняя версия: 0.1.2

Дата: 8 апр. 2022 г.

Mahout Distribution

org.apache.mahout : apache-mahout-distribution

Distribution Package

Последняя версия: 14.1

Дата: 16 июл. 2020 г.

mahout

com.walterjwhite.java.dependencies : apache-mahout

dependencies

Последняя версия: 0.0.17

Дата: 2 сент. 2019 г.

mahout

com.walterjwhite.infrastructure.dependencies : apache-mahout

dependencies

Последняя версия: 0.0.15

Дата: 18 дек. 2017 г.

Mallet

com.sksamuel.mallet : mallet

Последняя версия: 1.0.4

Дата: 18 мар. 2019 г.

julielab-ranklib-mallet

de.julielab : julielab-ranklib-mallet

The Parent POM for all JULIE Lab projects.

Последняя версия: 1.0.0

Дата: 26 янв. 2021 г.

DMNBtext

nz.ac.waikato.cms.weka : DMNBtext

Class for building and using a Discriminative Multinomial Naive Bayes classifier. For more information see: Jiang Su,Harry Zhang,Charles X. Ling,Stan Matwin: Discriminative Parameter Learning for Bayesian Networks. In: ICML 2008', 2008.

Последняя версия: 1.0.2

Дата: 26 апр. 2012 г.

iterativeAbsoluteErrorRegression

nz.ac.waikato.cms.weka : iterativeAbsoluteErrorRegression

Provides a regression scheme that uses Schlossmacher's iteratively reweighted least squares method to fit a model that minimizes absolute error. The scheme can be used with any base learner in WEKA that performs least-squares regression

Последняя версия: 1.0.0

Дата: 24 янв. 2016 г.

racedIncrementalLogitBoost

nz.ac.waikato.cms.weka : racedIncrementalLogitBoost

Classifier for incremental learning of large datasets by way of racing logit-boosted committees. For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets. In: Proceedings of the 5th International Conferenceon Discovery Science, 153-164, 2002.

Последняя версия: 1.0.2

Дата: 26 апр. 2012 г.

leastMedSquared

nz.ac.waikato.cms.weka : leastMedSquared

Implements a least median squared linear regression utilizing the existing weka LinearRegression class to form predictions. Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model. The basis of the algorithm is Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection.

Последняя версия: 1.0.2

Дата: 26 апр. 2012 г.

simpleCART

nz.ac.waikato.cms.weka : simpleCART

Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California.

Последняя версия: 1.0.2

Дата: 26 апр. 2012 г.

thresholdSelector

nz.ac.waikato.cms.weka : thresholdSelector

A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. The midpoint threshold is set so that a given performance measure is optimized. Currently this is the F-measure. Performance is measured either on the training data, a hold-out set or using cross-validation. In addition, the probabilities returned by the base learner can have their range expanded so that the output probabilities will reside between 0 and 1 (this is useful if the scheme normally produces probabilities in a very narrow range).

Последняя версия: 1.0.3

Дата: 25 апр. 2014 г.