Guest Editorial: Special Issue on Stream Learning

• Tipo: Outra Publicação em Revista Científica Internacional
• Data: 2023

Autores

Lu, J (Autor) Outra; João Gama (Autor) FEP; Yao, X (Autor) Outra; Minku, L (Autor) Outra

Revista

Título: IEEE Transactions on Neural Networks and Learning Systems

Vol. 34 Nº 10

Páginas: 6683-6685

ISSN: 2162-237X

Editora: IEEE

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 3