Recent research advances on interactive machine learning View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-04

AUTHORS

Liu Jiang, Shixia Liu, Changjian Chen

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. More... »

PAGES

401-417

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12650-018-0531-1

DOI

http://dx.doi.org/10.1007/s12650-018-0531-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110098061


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