  Interactive Machine Learning (IML) is an iterative learning process that
tightly couples a human with a machine learner, which is widely used by
researchers and practitioners to effectively solve a wide variety of real-world
application problems. Although recent years have witnessed the proliferation of
IML in the field of visual analytics, most recent surveys either focus on a
specific area of IML or aim to summarize a visualization field that is too
generic for IML. In this paper, we systematically review the recent literature
on IML and classify them into a task-oriented taxonomy built by us. We conclude
the survey with a discussion of open challenges and research opportunities that
we believe are inspiring for future work in IML.
