Guest Editorial: Special Issue on Stream Learning

被引：0

作者：

Lu, Jie ^{[1
]}

Gama, Joao ^{[2
]}

Yao, Xin ^{[3
]}

Minku, Leandro ^{[4
]}

机构：

[1] Univ Technol Sydney, Australian Artificial Intelligence Inst, Sydney, NSW 2007, Australia

[2] Univ Porto, Fac Econ, P-4099002 Porto, Portugal

[3] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China

[4] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, England

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2023年 / 34卷 / 10期

关键词：

Special issues and sections; Streaming media; Learning systems; Reinforcement learning;

D O I：

10.1109/TNNLS.2023.3304146

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：6683 / 6685

页数：3

共 21 条

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Brito da Silva, Leonardo Enzo ;

Rayapati, Nagasharath ;

Wunsch, Donald C., II .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) :6686-6700

[2] Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting [J].

Chen, Changlu ;

Liu, Yanbin ;

Chen, Ling ;

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) :6913-6925

[3] InOR-Net: Incremental 3-D Object Recognition Network for Point Cloud Representation [J].

Dong, Jiahua ;

Cong, Yang ;

Sun, Gan ;

Wang, Lixu ;

Lyu, Lingjuan ;

Li, Jun ;

Konukoglu, Ender .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) :6955-6967

[4] Multiagent Reinforcement Learning With Heterogeneous Graph Attention Network [J].

Du, Wei ;

Ding, Shifei ;

Zhang, Chenglong ;

Shi, Zhongzhi .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) :6851-6860

[5] IWDA: Importance Weighting for Drift Adaptation in Streaming Supervised Learning Problems [J].

Fedeli, Filippo ;

Metelli, Alberto Maria ;

Trovo, Francesco ;

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) :6813-6823

[6] Adaptation for Automated Drift Detection in Electromechanical Machine Monitoring [J].

Green, Daisy H. ;

Langham, Aaron W. ;

Agustin, Rebecca A. ;

Quinn, Devin W. ;

Leeb, Steven B. .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) :6768-6782

[7] A Multilayer Framework for Online Metric Learning [J].

Li, Wenbin ;

Liu, Yanfang ;

Huo, Jing ;

Shi, Yinghuan ;

Gao, Yang ;

Wang, Lei ;

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 34 (10) :6701-6713

[8] Interval Dominance-Based Feature Selection for Interval-Valued Ordered Data [J].

Li, Wentao ;

Zhou, Haoxiang ;

Xu, Weihua ;

Wang, Xi-Zhao ;

Pedrycz, Witold .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) :6898-6912

[9] Online Passive-Aggressive Active Learning for Trapezoidal Data Streams [J].

Liu, Yanfang ;

Fan, Xiaocong ;

Li, Wenbin ;

Gao, Yang .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) :6725-6739

[10] An Online Active Broad Learning Approach for Real-Time Safety Assessment of Dynamic Systems in Nonstationary Environments [J].

Liu, Zeyi ;

Zhang, Yi ;

Ding, Zhongjun ;

He, Xiao .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) :6714-6724

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