Details

Autor(en) / Beteiligte

• Zhang, Jing

Titel

Knowledge Learning With Crowdsourcing: A Brief Review and Systematic Perspective

Ist Teil von

• IEEE/CAA journal of automatica sinica, 2022-05, Vol.9 (5), p.749-762

Ort / Verlag

Piscataway: Chinese Association of Automation (CAA)

Erscheinungsjahr

2022

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Big 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.