Category Shift Learning
Category Shift Learning is a cognitive development framework that specifies the mechanisms by which individuals reorganize conceptual representations in response to changes in category structure or membership criteria during the process of categorization learning.
42
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3.3K
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115
Authors
49
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2017–2026
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A unifying view on dataset shift in classification
Jose G. Moreno-Torres, Troy Raeder, Rocío Aláiz-Rodríguez et al. · Pattern Recognition · 2011 · 962 citations
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Ruijia Xu, Ziliang Chen, Wangmeng Zuo et al. · 2018 · 414 citations
Universal Domain Adaptation through Self Supervision
Kuniaki Saito, Donghyun Kim, Stan Sclaroff et al. · arXiv (Cornell University) · 2020 · 163 citations · Full text
Dimensional Relevance Shifts in Category Learning
John K. Kruschke · Connection Science · 1996 · 135 citations
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Peking University
Beijing, China
3 h-index
7 publications
256 citations
Indiana University Bloomington
Bloomington, United States
3 h-index
3 publications
333 citations
University of Iowa
Iowa City, United States
3 h-index
8 publications
400 citations
Dalhousie University
Halifax, Canada
2 h-index
3 publications
189 citations
King University
Bristol, United States
1 h-index
3 publications
138 citations
Arizona State University
Tempe, United States
1 h-index
2 publications
30 citations
VA San Diego Healthcare System
San Diego, United States
1 h-index
1 publications
29 citations
Technion – Israel Institute of Technology
Haifa, Israel
1 h-index
2 publications
52 citations
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3 publications · 90 citations
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Repository · 3 publications · 231 citations
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2 publications · 142 citations
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2 publications · 46 citations
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2 publications · 110 citations
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