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Integrating Iterative Machine Teaching and Active Learning into the Machine Learning Loop
2021
Procedia Computer Science
Scholars and practitioners are defining new types of interactions between humans and machine learning algorithms that we can group under the umbrella term of Human-in-the-Loop Machine Learning (HITL-ML). This paper is focused on implementing two approaches to this topic-Iterative Machine Teaching (iMT) and Active Learning (AL)-and analyzing how to integrate them in the learning loop. iMT is a variation of Machine Teaching in which a machine acts as a teacher that tries to transfer knowledge to
doi:10.1016/j.procs.2021.08.057
fatcat:axgjibqgfbgdjguyvmpcckbpmu