Human-in-the-loop Classification
Human-in-the-Loop Classification is a classification paradigm within machine learning and data science that integrates human expertise as an active component in the iterative training, refinement, and decision-making processes of automated classification systems.
33
Publications
1.7K
Citations
170
Authors
86
Institutions
Publications per year
2017–2026
28
Parents
33
Humans can decipher adversarial images
Nature Communications · 2019 · 72 citations · Full text
Category detection using hierarchical mean shift
Pavan Kumar Vatturi, Weng‐Keen Wong · 2009 · 60 citations
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86

The University of Queensland
Brisbane, Australia
2 h-index
5 publications
142 citations
Temple University
Philadelphia, United States
2 h-index
8 publications
248 citations
California State University, Fresno
Fresno, United States
1 h-index
1 publications
20 citations
Virginia Tech
Blacksburg, United States
1 h-index
1 publications
23 citations

Université de Toulon
La Garde, France
1 h-index
1 publications
17 citations
Tokyo University of Information Sciences
Chiba, Japan
1 h-index
2 publications
46 citations

Data61
Sydney, Australia
1 h-index
4 publications
280 citations

Universitat Pompeu Fabra
Barcelona, Spain
1 h-index
1 publications
41 citations
1–8 of 86
Conference · 2 publications · 34 citations
2
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1 publications · 19 citations
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1 publications · 18 citations
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1 publications · 23 citations
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TIIS · 1 publications · 36 citations
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