A Generic Human-Machine Annotation Framework Based on Dynamic Cooperative Learning

IEEE Transactions on Cybernetics, pp. 1230-1239, 2019.

Cited by: 1|Bibtex|Views100|DOI:https://doi.org/10.1109/TCYB.2019.2901499
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Other Links: pubmed.ncbi.nlm.nih.gov|academic.microsoft.com|dblp.uni-trier.de

Abstract:

The task of obtaining meaningful annotations is a tedious work, incurring considerable costs and time consumption. Dynamic active learning and cooperative learning are recently proposed approaches to reduce human effort of annotating data with subjective phenomena. In this paper, we introduce a novel generic annotation framework, with the...More

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