Download JAR files tagged by representative with all dependencies
color-thief from group de.androidpit (version 1.1.2)
Grabs the dominant color or a representative color palette from an image. A very fast Java port of Lokesh Dhakar's JavaScript version.
Artifact color-thief
Group de.androidpit
Version 1.1.2
Last update 18. October 2018
Organization not specified
URL https://github.com/SvenWoltmann/color-thief-java
License Creative Commons Attribution 2.5
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group de.androidpit
Version 1.1.2
Last update 18. October 2018
Organization not specified
URL https://github.com/SvenWoltmann/color-thief-java
License Creative Commons Attribution 2.5
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
storyclmsdk from group com.storyclm (version 1.0.5)
StoryCLM — a digital-platform developed by BREFFI, allowing you to create interactive presentations with immediate feedback on the change in the customer perception of the brand and the representative’s activity.
3 downloads
Artifact storyclmsdk
Group com.storyclm
Version 1.0.5
Last update 29. November 2017
Organization not specified
URL https://github.com/storyclm/Java-SDK
License The MIT License
Dependencies amount 5
Dependencies guava, okhttp, retrofit, converter-gson, gson,
There are maybe transitive dependencies!
Group com.storyclm
Version 1.0.5
Last update 29. November 2017
Organization not specified
URL https://github.com/storyclm/Java-SDK
License The MIT License
Dependencies amount 5
Dependencies guava, okhttp, retrofit, converter-gson, gson,
There are maybe transitive dependencies!
multiBoostAB from group nz.ac.waikato.cms.weka (version 1.0.2)
Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution.
For more information, see
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
Group: nz.ac.waikato.cms.weka Artifact: multiBoostAB
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0 downloads
Artifact multiBoostAB
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/multiBoostAB
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/multiBoostAB
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
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