Guest Editorial: Special Issue on Stream Learning

• Type: Another Publication in an International Scientific Journal
• Date: 2023

Authors

Lu, J (Author) Other; João Gama (Author) FEP; Yao, X (Author) Other; Minku, L (Author) Other

Journal

Title: IEEE Transactions on Neural Networks and Learning Systems

Vol. 34 No. 10

Pages: 6683-6685

ISSN: 2162-237X

Publisher: IEEE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-5B3

DOI: 10.1109/tnnls.2023.3304146

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 3