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Journal of Visualization
Erschienen in: Journal of Visualization 2/2019

21.11.2018 | Regular Paper

Recent research advances on interactive machine learning

verfasst von: Liu Jiang, Shixia Liu, Changjian Chen

Erschienen in: Journal of Visualization | Ausgabe 2/2019

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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.

Graphical abstract

Vorheriger Artikel A visual recommendation system for co-authorship social networks (ChinaVis 2018)
Nächster Artikel A survey of visualization for smart manufacturing

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Metadaten
Titel
Recent research advances on interactive machine learning
verfasst von
Liu Jiang
Shixia Liu
Changjian Chen
Publikationsdatum
21.11.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Visualization / Ausgabe 2/2019
Print ISSN: 1343-8875
Elektronische ISSN: 1875-8975
DOI
https://doi.org/10.1007/s12650-018-0531-1

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