Class-Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning System

Author:

Du Jie1ORCID,Liu Peng2,Vong Chi-Man2ORCID,Chen Chuangquan3ORCID,Wang Tianfu1ORCID,Chen C. L. Philip4ORCID

Affiliation:

1. Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China

2. Department of Computer and Information Science, University of Macau, SAR, China

3. Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China

4. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

Funder

National Natural Science Foundation of China

GuangDong Basic and Applied Basic Research Foundation

Foundation for Distinguished Young Talents in Higher Education of Guangdong

Science and Technology Development Fund, Macau SAR

Wuyi University External Fund

University of Macau

Science and Technology Major Project of Guangzhou

Program for Guangdong Introducing Innovative and Entrepreneurial Teams

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

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1. Self-organizing broad network with frequency-domain analysis;Engineering Applications of Artificial Intelligence;2024-11

2. A session-incremental broad learning system for motor imagery EEG classification;Biomedical Signal Processing and Control;2024-11

3. Cross-Domain Class Incremental Broad Network for Continuous Diagnosis of Rotating Machinery Faults Under Variable Operating Conditions;IEEE Transactions on Industrial Informatics;2024-04

4. Dynamic Neural Network Structure: A Review for Its Theories and Applications;IEEE Transactions on Neural Networks and Learning Systems;2024

5. An Incremental-Self-Training-Guided Semi-Supervised Broad Learning System;IEEE Transactions on Neural Networks and Learning Systems;2024

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