Addressing Intermediate Verification Latency in Online Learning Through Immediate Pseudo-labeling and Oriented Synthetic Correction-Reference-Cited by-同舟云学术

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Addressing Intermediate Verification Latency in Online Learning Through Immediate Pseudo-labeling and Oriented Synthetic Correction

Published:2024-06-30 Issue: Volume:80 Page:1-10
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
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Author:

Zhong Zixin¹,Song Liyan²,Tang Fengzhen³,Yuan Bo¹

Affiliation:

1. Southern University of Science and Technology,Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,China

2. Harbin Institute of Technology,Faculty of Computing,Harbin,China

3. Chinese Academy of Sciences,State Key Laboratory of Robotics, Shenyang Institute of Automation,Shenyang,China

Funder

State Key Laboratory of Robotics

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650783.pdf?arnumber=10650783

Reference36 articles.

1. Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams

2. Characterizing concept drift

3. Learning under Concept Drift: A Review

4. A survey on concept drift adaptation

5. Classification of Evolving Data Streams with Infinitely Delayed Labels

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