Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective-Reference-Cited by-同舟云学术

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Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective

Published:2022-05 Issue:5 Volume:9 Page:749-762
ISSN:2329-9266
Container-title:IEEE/CAA Journal of Automatica Sinica
language:
Short-container-title:IEEE/CAA J. Autom. Sinica

Author:

Zhang Jing¹

Affiliation:

1. School of Computer Science and Engineering, Nanjing University of Science and Technology,Nanjing,China,210094

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Information Systems,Control and Systems Engineering,Control and Optimization

Link

http://xplorestaging.ieee.org/ielx7/6570654/9763463/09763477.pdf?arnumber=9763477

Reference118 articles.

1. Improving consensus accuracy via Z-score and weighted voting;jung;Proc 3rd Human Comput Workshop,0

2. Multi-label truth inference for crowdsourcing using mixture models;zhang;IEEE Trans Knowl Data Eng,2021

3. Aggregating ordinal labels from crowds by minimax conditional entropy;zhou;Proc 31st Int Conf Mach Learn,0

4. Exploiting worker correlation for label aggregation in crowdsourcing;li;Int Conf Mach Learn,0

5. Spectral methods meet EM: A provably optimal algorithm for crowdsourcing;zhang;J Mach Learn Res,2016

Cited by 25 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Instance redistribution-based label integration for crowdsourcing;Information Sciences;2024-07

2. Age-at-Death Estimation based on Symbolic Regression Ensemble Learning from Multiple Annotations;2024 IEEE Congress on Evolutionary Computation (CEC);2024-06-30

3. Certainty weighted voting-based noise correction for crowdsourcing;Pattern Recognition;2024-06

4. A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration;IEEE/CAA Journal of Automatica Sinica;2024-04

5. Worker similarity-based noise correction for crowdsourcing;Information Systems;2024-03

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