图 书 馆
流媒体多标签分类Streaming Multi-label Classification |
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| 课程网址: | http://videolectures.net/wapa2011_read_classification/ |
| 主讲教师: | Jesse Read |
| 开课单位: | 巴黎综合理工学院 |
| 开课时间: | 2011-11-11 |
| 课程语种: | 英语 |
| 中文简介: |  本文提出了一个新的实验框架，用于研究多标签演变流分类，并采用有效的方法将流场景中的最佳实践与多标签分类中的最佳实践相结合。许多现实世界中的问题都涉及到可以被视为多标签数据流的数据。存在在非流传输方案中用于多标签分类的有效方法。但是，在不断发展的流方案中学习更具挑战性，因为学习者必须能够使用有限的时间和内存来适应变化。我们提出了一个新的实验软件，该软件扩展了MOA框架。大规模在线分析（MOA）是一种软件环境，用于实施算法并运行实验，以从不断发展的数据流中进行在线学习。它是根据GNU GPL许可发布的。 |
| 课程简介: | This paper presents a new experimental framework for studying multi-label evolving stream classification, with efficient methods that combine the best practices in streaming scenarios with the best practices in multi-label classification. Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. We present a new experimental software that extends the MOA framework. Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. It is released under the GNU GPL license. |
| 关 键 词: | 在线学习; 多标签分类 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-16：zyk |
| 最后编审: | 2021-06-25：zyk |
| 阅读次数: | 63 |