| 书目名称 | Learning in Non-Stationary Environments | | 副标题 | Methods and Applicat | | 编辑 | Moamar Sayed-Mouchaweh,Edwin Lughofer | | 视频video | | | 概述 | Shows the state-of-the-art in dynamic learning, discussing advanced aspects and concepts.Presenting open problems and future challenges in this field.Examines the links between the different methods a | | 图书封面 |  | | 描述 | .Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. . .Learning in Non-Stationary Environments: Methods and Applications .offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. . .Rather than rely on a mathematical theorem/proof style, the editors highlight numerous fig | | 出版日期 | Book 2012 | | 关键词 | Dynamic learning; Knowledge extraction; adaptive modeling; data streams; drifts and shifts; dynamic dimen | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4419-8020-5 | | isbn_softcover | 978-1-4899-9340-3 | | isbn_ebook | 978-1-4419-8020-5 | | copyright | Springer Science+Business Media New York 2012 |
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发表于 2025-3-21 18:57:23
