Crowd-Assisted Machine Learning: Current Issues and Future Directions

Jiangtao Wang; Yasha Wang; Qin Lv

doi:10.1109/MC.2018.2890174

Crowd-Assisted Machine Learning: Current Issues and Future Directions

Jiangtao Wang
, Yasha Wang
, Qin Lv

Peking University
University of Colorado
Lancaster University

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

Many intelligent computing tasks cannot be fully handled by machines. This article reviews crowd-assisted machine learning (ML) opportunities for future research, identifies the main challenges of ML with pure machine intelligence, and proposes a crowd-assisted framework, Crowd4ML

Original language	English
Pages (from-to)	46-53
Number of pages	8
Journal	Computer
Volume	52
Issue number	1
DOIs	https://doi.org/10.1109/MC.2018.2890174
Publication status	Published - 1 Jan 2019
Externally published	Yes

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title = "Crowd-Assisted Machine Learning: Current Issues and Future Directions",

abstract = "Many intelligent computing tasks cannot be fully handled by machines. This article reviews crowd-assisted machine learning (ML) opportunities for future research, identifies the main challenges of ML with pure machine intelligence, and proposes a crowd-assisted framework, Crowd4ML",

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Crowd-Assisted Machine Learning: Current Issues and Future Directions

Abstract

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