Showing 5 open source projects for "qmmp-plugin-pack"

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    Velocity server

    Velocity server

    The modern, next-generation Minecraft server proxy

    ...The software is designed with a focus on performance, scalability, and modern architecture, allowing it to handle thousands of simultaneous players efficiently. Velocity also includes a plugin API that allows developers to extend the proxy with custom functionality and integrate it with existing server tools. Compared with older proxy solutions, the project emphasizes improved performance, reliability, and cleaner software design.
    Downloads: 3 This Week
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  • 2
    elasticsearch-learning-to-rank

    elasticsearch-learning-to-rank

    Plugin to integrate Learning to Rank

    The Elasticsearch Learning to Rank plugin uses machine learning to improve search relevance ranking. It's powering search at places like Wikimedia Foundation and Snagajob.
    Downloads: 0 This Week
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  • 3
    Genetic Oversampling Weka Plugin

    Genetic Oversampling Weka Plugin

    A Weka Plugin that uses a Genetic Algorithm for Data Oversampling

    Weka genetic algorithm filter plugin to generate synthetic instances. This Weka Plugin implementation uses a Genetic Algorithm to create new synthetic instances to solve the imbalanced dataset problem. See my master thesis available for download, for further details.
    Downloads: 1 This Week
    Last Update:
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  • 4
    The Information Extraction Plugin allows the use of information extraction techniques within RapidMiner. It can be seen as an interface between natural language and IE- or datamining-methods, by extracting interesting information out of documents.
    Downloads: 0 This Week
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  • 5
    This is a RapidMiner extension replacing the current Weka-Plugin with the updated 3.7.3 Weka-Version. This is basically a branch of the 3.7.3 Version of WEKA wrapped into the old extension. New Features Include: -All the Features of the 3.7.3 Weka Package -Multi-Threaded ensemble learning -An enhancement on the popular RandomForest Learner based on "Dynamic Integration with Random Forests" by Tsymbal et al. 2006 and "Improving Random Forests" by Robnik-Sikonja 2004.
    Downloads: 0 This Week
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