Download JAR files tagged by learning with all dependencies

LBFGS from group com.github.thssmonkey (version 1.0.4)

Limited-memory BFGS (L-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm using a limited amount of computer memory. It is a popular algorithm for parameter estimation in machine learning. The algorithm's target problem is to minimize f(x) over unconstrained values of the real-vector x where f is a differentiable scalar function.

Group: com.github.thssmonkey Artifact: LBFGS
Show all versions Show documentation Show source

0 downloads

Artifact LBFGS
Group com.github.thssmonkey
Version 1.0.4
Last update 16. May 2019
Organization not specified
URL https://github.com/thssmonkey/LBFGS
License The Apache Software License, Version 2.0
Dependencies amount 4
Dependencies flink-scala_${scala.binary.version}, flink-streaming-scala_${scala.binary.version}, flink-clients_${scala.binary.version}, flink-ml_${scala.binary.version},
There are maybe transitive dependencies!

OpenJazariLibrary from group com.github.hakmesyo (version 1.0.1)

Open Jazari Library for Java (OJL4J), formerly known as Open Cezeri Library is a Java based Matrix, Vectorization, Image Processing, Machine Learning (Weka + DL4J), Data Visualization and Analysis DSL (Domain Specific Language) which enables developers finish the code with almost single line of code.

Group: com.github.hakmesyo Artifact: OpenJazariLibrary
Show all versions Show documentation Show source

0 downloads

Artifact OpenJazariLibrary
Group com.github.hakmesyo
Version 1.0.1
Last update 25. December 2018
Organization not specified
URL https://github.com/hakmesyo/Open-Jazari-Library
License MIT License
Dependencies amount 16
Dependencies commons-lang3, weka-stable, ij, Jama, opencv-platform, opencsv, filters, jwave, webcam-capture, lti-civil-no-swt, rxtx, jna, open_kinect, processing_core, deeplearning4j-core, nd4j-api,
There are maybe transitive dependencies!

stackingC from group nz.ac.waikato.cms.weka (version 1.0.4)

Implements StackingC (more efficient version of stacking). For more information, see A.K. Seewald: How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness. In: Nineteenth International Conference on Machine Learning, 554-561, 2002. Note: requires meta classifier to be a numeric prediction scheme

Group: nz.ac.waikato.cms.weka Artifact: stackingC
Show all versions Show documentation Show source

0 downloads

Artifact stackingC
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 19. November 2018
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/stackingC
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

XMeans from group nz.ac.waikato.cms.weka (version 1.0.6)

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.

Group: nz.ac.waikato.cms.weka Artifact: XMeans
Show all versions Show documentation Show source

6 downloads

Artifact XMeans
Group nz.ac.waikato.cms.weka
Version 1.0.6
Last update 28. February 2018
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/XMeans
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

extraTrees from group nz.ac.waikato.cms.weka (version 1.0.2)

Package for generating a single Extra-Tree. Use with the RandomCommittee meta classifier to generate an Extra-Trees forest for classification or regression. This classifier requires all predictors to be numeric. Missing values are not allowed. Instance weights are taken into account. For more information, see Pierre Geurts, Damien Ernst, Louis Wehenkel (2006). Extremely randomized trees. Machine Learning. 63(1):3-42.

Group: nz.ac.waikato.cms.weka Artifact: extraTrees
Show all versions Show documentation Show source

0 downloads

Artifact extraTrees
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 03. December 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/extraTrees
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

probabilityCalibrationTrees from group nz.ac.waikato.cms.weka (version 1.0.0)

Provides probability calibration trees (PCTs) for local calibration of class probability estimates. To achieve calibration of a base learner, the PCT class must be used as the meta learner in the CascadeGeneralization class, which is also included in this package. The classifier to be calibrated must be used as the base learner in the CascadeGeneralization class. The CascadeGeneralization class can also be used independently to perform CascadeGeneralization for ensemble learning. The code for PCTs is largely the same as the LMT code for growing logistic model trees. For more details, see the ACML paper on probability calibration trees.

Group: nz.ac.waikato.cms.weka Artifact: probabilityCalibrationTrees
Show documentation Show source

2 downloads

Artifact probabilityCalibrationTrees
Group nz.ac.waikato.cms.weka
Version 1.0.0
Last update 26. November 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/probabilityCalibrationTrees
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

ensemblesOfNestedDichotomies from group nz.ac.waikato.cms.weka (version 1.0.6)

A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. In: PKDD, 84-95, 2005. Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for multi-class problems. In: Twenty-first International Conference on Machine Learning, 2004.

Group: nz.ac.waikato.cms.weka Artifact: ensemblesOfNestedDichotomies
Show all versions Show documentation Show source

0 downloads

Artifact ensemblesOfNestedDichotomies
Group nz.ac.waikato.cms.weka
Version 1.0.6
Last update 21. February 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/ensemblesOfNestedDichotomies
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

teachingbox-core from group org.sf.teachingbox (version 1.2.3)

The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners.

Group: org.sf.teachingbox Artifact: teachingbox-core
Show all versions Show documentation Show source

1 downloads

Artifact teachingbox-core
Group org.sf.teachingbox
Version 1.2.3
Last update 08. November 2016
Organization TeachingBox
URL http://sourceforge.net/projects/teachingbox/
License GNU General Public License, Version 3
Dependencies amount 14
Dependencies guava, jfreechart, surfaceplotter, log4j, commons-lang3, commons-math3, colt, junit, thrift, concurrent, encog-core, weka-stable, nrjavaserial, redstone,
There are maybe transitive dependencies!

jclal from group net.sf.jclal (version 1.1)

JCLAL is a software system for Active Learning research, developed in the Java programming language. It provides a high-level software framework and a robust object-oriented design that allows an easy use, extention, modification and reusability. It includes the most relevant query strategies proposed on the single-label and multi-label learning contexts.

Group: net.sf.jclal Artifact: jclal
Show documentation Show source

0 downloads

Artifact jclal
Group net.sf.jclal
Version 1.1
Last update 01. April 2016
Organization University of Holguín, Cuba- University of Cordoba, Spain
URL http://jclal.sf.net
License GNU General Public License 3
Dependencies amount 21
Dependencies commons-beanutils, commons-beanutils-core, commons-collections, commons-configuration, commons-digester, commons-lang, commons-logging, epsgraphics, hamcrest-core, java-cup, jcommon, jfreechart, jfreesvg, lucene-core, mail, moa, mulan, multiInstanceFilters, multiInstanceLearning, pentaho-package-manager, weka-dev,
There are maybe transitive dependencies!

sfac from group net.sf.sfac (version 0.8)

This project is a simple Java-Swing framework giving strategies to implements usual graphical application concern (action management, application state, internationalization ...) The framework is easy to learn and use and is targeted for small Swing applications, it can also be a good start for learning swing. In addition, this project proposes useful Swing components like an advanced layout manager, a multi-column sorting table, object editor...

Group: net.sf.sfac Artifact: sfac
Show all versions

There is no JAR file uploaded. A download is not possible! Please choose another version.

0 downloads

Artifact sfac
Group net.sf.sfac
Version 0.8
Last update 13. March 2016
Organization not specified
URL http://sfac.sourceforge.net
License Apache 2
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!

Page 148 from 152 (items total 1512)