Supervised Learning
Supervised Learning is a machine learning and statistical learning paradigm in which a model is trained on labeled input–output pairs to learn a mapping from inputs to outputs that generalizes to unseen data.
10.1K
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1.1M
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25.7K
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4K
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2017–2026
5.9K
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Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams · Nature · 1986 · 30.9K citations
Greedy function approximation: A gradient boosting machine.
Jerome H. Friedman · The Annals of Statistics · 2001 · 29K citations · Full text
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang, Phillip Isola, Alexei A. Efros et al. · 2018 · 12.4K citations
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Google (United States)
Mountain View, United States
66 h-index
464 publications
116.9K citations
Carnegie Mellon University
Pittsburgh, United States
62 h-index
317 publications
91.4K citations
Tsinghua University
Beijing, China
58 h-index
431 publications
30.8K citations
University of California, Berkeley
Berkeley, United States
57 h-index
215 publications
72.5K citations
Stanford University
Stanford, United States
56 h-index
285 publications
85.1K citations

Chinese Academy of Sciences
Beijing, China
56 h-index
476 publications
24.5K citations
Nanjing University
Nanjing, China
54 h-index
283 publications
33K citations
Nanyang Technological University
Singapore, Singapore
53 h-index
208 publications
23.1K citations
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Repository · 1K publications · 168.2K citations
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TPAMI · 151 publications · 32.7K citations
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131 publications · 12.6K citations
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201 publications · 10.7K citations
51
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88 publications · 22K citations
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