Active Learning For Verification
Active Learning for Verification is a machine learning approach that iteratively selects the most informative samples for manual verification to improve classifier performance while minimizing labeling effort in the context of reducing false positives.
7
Publications
1.5K
Citations
28
Authors
15
Institutions
Publications per year
2017–2026
3
Parents
7
A scalable active framework for region annotation in 3D shape collections
Li Yi, Vladimir G. Kim, Duygu Ceylan et al. · ACM Transactions on Graphics · 2016 · 1.3K citations
RAVEN - active learning of link specifications
Axel-Cyrille Ngonga Ngomo, Jens Lehmann, Sören Auer et al. · 2011 · 59 citations
PhishBox: An Approach for Phishing Validation and Detection
Jhen-Hao Li, Sheng‐De Wang · 2017 · 25 citations
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University of British Columbia
Vancouver, Canada
1 h-index
2 publications
2.6K citations
Universiti Putra Malaysia
Seri Kembangan, Malaysia
1 h-index
1 publications
27 citations
Aalborg University
Aalborg, Denmark
1 h-index
2 publications
46 citations
Leipzig University
Leipzig, Germany
1 h-index
4 publications
236 citations
Yuan Ze University
Taoyuan District, Taiwan
1 h-index
2 publications
54 citations

National Taiwan University
Taipei, Taiwan
1 h-index
2 publications
50 citations
The University of Texas at Austin
Austin, United States
1 h-index
1 publications
1.3K citations
Toyota Technological Institute at Chicago
Chicago, United States
1 h-index
1 publications
1.3K citations
1–8 of 15
1 publications · 27 citations
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TOG · 1 publications · 1.3K citations
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1 publications · 19 citations
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1 publications · 10 citations
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