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Guest Editorial: Special Issue on Stream Learning
DOI:10.1109/TNNLS.2023.3304146.png)
摘要
En 中文
In recent years, learning from streaming data, commonly known as stream learning, has enjoyed tremendous growth and shown a wealth of development at both the conceptual and application levels. Stream learning is highly visible in both the machine learning and data science fields and has become a hot new direction in research. Advancements in stream learning include learning with concept drift detection, that includes whether a drift has occurred; understanding where, when, and how a drift occurs; adaptation by actively or passively updating models; and online learning, active learning, incremental learning, and reinforcement learning in data streaming situations.
Keyword:
Special issues and sections
Streaming media
Learning systems
Reinforcement learning
期刊
IF:
8.9
论文数:
7.5K
被引数:
7.2W
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