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Recent research advances on interactive machine learning

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Abstract

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.

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  1. National Engineering Lab for Big Data Software, and School of Software, Tsinghua University, Beijing, China

    Liu Jiang, Shixia Liu & Changjian Chen

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  1. Liu Jiang
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Correspondence to Shixia Liu.

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Jiang, L., Liu, S. & Chen, C. Recent research advances on interactive machine learning. J Vis 22, 401–417 (2019). https://doi.org/10.1007/s12650-018-0531-1

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