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MOA：一个实时分析开源框架
MOA: a Real-time Analytics Open Source Framework |
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| 课程网址: |
http://videolectures.net/ecmlpkdd2011_pfahringer_analytics/
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| 主讲教师: |
Bernhard Pfahringer
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| 开课单位: |
怀卡托大学
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| 开课时间: |
2011-10-03
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| 课程语种: |
英语
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| 中文简介: |
大规模在线分析（MOA）是一种软件环境，用于实现算法和运行实验，以便从不断发展的数据流中进行在线学习。 MOA旨在解决将现有技术算法的实施扩展到现实世界数据集大小以及使基准流设置中的算法具有可比性的挑战性问题。它包含一系列离线和在线算法，用于分类，聚类和图形挖掘以及评估工具。对于研究人员而言，该框架可以深入了解不同方法的优缺点，并允许通过数据馈送的存储，共享和可重复设置创建基准流数据集。从业者可以使用该框架轻松比较算法并将其应用于现实世界的数据集和设置。 MOA支持与怀卡托知识分析环境WEKA的双向互动。除了提供用于评估和比较的算法和度量之外，MOA还可以通过新的贡献轻松扩展，并允许创建基准测试场景。
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| 课程简介: |
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problems of scaling up the implementation of state of the art algorithms to real world dataset sizes and of making algorithms comparable in benchmark streaming settings. It contains a collection of offline and online algorithms for classification, clustering and graph mining as well as tools for evaluation. For researchers the framework yields insights into advantages and disadvantages of different approaches and allows for the creation of benchmark streaming data sets through stored, shared and repeatable settings for the data feeds. Practitioners can use the framework to easily compare algorithms and apply them to real world data sets and settings. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis. Besides providing algorithms and measures for evaluation and comparison, MOA is easily extensible with new contributions and allows for the creation of benchmark scenarios.
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| 关 键 词: |
大规模在线分析; 软件环境; 数据流
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-03：lxf
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| 阅读次数: |
337
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