Recent Research Advances on Interactive Machine Learning
Machine Learning
2018-11-13 v1 Machine Learning
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.
Cite
@article{arxiv.1811.04548,
title = {Recent Research Advances on Interactive Machine Learning},
author = {Liu Jiang and Shixia Liu and Changjian Chen},
journal= {arXiv preprint arXiv:1811.04548},
year = {2018}
}