CASIA OpenIR  > 学术期刊  > IEEE/CAA Journal of Automatica Sinica
Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective
Jing Zhang
Source PublicationIEEE/CAA Journal of Automatica Sinica
AbstractBig data have the characteristics of enormous volume, high velocity, diversity, value-sparsity, and uncertainty, which lead the knowledge learning from them full of challenges. With the emergence of crowdsourcing, versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning process. During the past thirteen years, researchers in the AI community made great efforts to remove the obstacles in the field of learning from crowds. This concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data, models, and learning processes. In addition to reviewing existing important work, the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work, which will light up the way for new researchers and encourage them to pursue new contributions.
KeywordCrowdsourcing data fusion learning from crowds learning paradigms learning with uncertainty
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Document Type期刊论文
Collection学术期刊_IEEE/CAA Journal of Automatica Sinica
Recommended Citation
GB/T 7714
Jing Zhang. Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(5):749-762.
APA Jing Zhang.(2022).Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective.IEEE/CAA Journal of Automatica Sinica,9(5),749-762.
MLA Jing Zhang."Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective".IEEE/CAA Journal of Automatica Sinica 9.5(2022):749-762.
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