TY - JOUR
T1 - Guest Editorial: Special Issue on Stream Learning
AU - LU, Jie
AU - GAMA, Joao
AU - YAO, Xin
AU - MINKU, Leandro
PY - 2023/10
Y1 - 2023/10
N2 - 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. © 2012 IEEE.
AB - 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. © 2012 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=85174928001&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2023.3304146
DO - 10.1109/TNNLS.2023.3304146
M3 - Editorial/Preface (Journal)
SN - 2162-237X
VL - 34
SP - 6683
EP - 6685
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 10
ER -