Semi-supervised Detection
Semi-Supervised Detection is a method of incident detection that belongs to the class of semi-supervised learning approaches, distinguished by its utilization of a small set of labeled instances alongside a large pool of unlabeled data to identify anomalies or security events while minimizing reliance on costly labeled training samples.
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28.1K
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2.1K
Authors
702
Institutions
Publications per year
2017–2026
437
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552
Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data
Eric Bair, Robert Tibshirani · PLoS Biology · 2004 · 761 citations · Full text
Medicine, Cancer Biology, Oncology +17
Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos
Huy H. Nguyen, Fuming Fang, Junichi Yamagishi et al. · 2019 · 523 citations · Full text
1–5 of 552
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Hong Kong University of Science and Technology, Hong Kong SAR China
4 publications · 200 citations
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1–5 of 2.1K
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Chinese Academy of Sciences
Beijing, China
16 h-index
50 publications
1.6K citations
Tsinghua University
Beijing, China
13 h-index
27 publications
1.8K citations

Northwestern Polytechnical University
Xi'an, China
10 h-index
28 publications
775 citations

Shanghai Jiao Tong University
Shanghai, China
10 h-index
25 publications
704 citations
Carnegie Mellon University
Pittsburgh, United States
10 h-index
18 publications
2.6K citations
Tencent (China)
Shenzhen, China
9 h-index
17 publications
764 citations
Tianjin University
Tianjin, China
9 h-index
35 publications
3.6K citations

Xidian University
Xi'an, China
9 h-index
21 publications
761 citations
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Repository · 31 publications · 1.2K citations
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20 publications · 955 citations
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14 publications · 452 citations
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11 publications · 593 citations
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Conference · 10 publications · 464 citations
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