Pool-based Active Learning
Pool-Based Active Learning is a type of active learning paradigm in which the algorithm selects the most informative instances from a fixed, pre-existing pool of unlabeled data for annotation, thereby distinguishing itself from streaming or query-synthesis approaches by its reliance on a static candidate set.
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2017–2026
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A bound on the label complexity of agnostic active learning
Steve Hanneke · 2007 · 250 citations
Online Choice of Active Learning Algorithms
Yoram Baram, Ran El‐Yaniv, Kobi Luz · 2003 · 187 citations
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Peking University
Beijing, China
3 h-index
5 publications
186 citations
Tokyo Institute of Technology
Tokyo, Japan
3 h-index
4 publications
148 citations
Australian Centre for Robotic Vision
Brisbane, Australia
2 h-index
9 publications
621 citations

King Abdulaziz University
Jeddah, Saudi Arabia
2 h-index
3 publications
193 citations
Georgia Institute of Technology
Atlanta, United States
2 h-index
4 publications
84 citations
Tufts University
Medford, United States
2 h-index
4 publications
460 citations
University of California, Berkeley
Berkeley, United States
2 h-index
5 publications
140 citations
Technical University of Denmark
Kongens Lyngby, Denmark
2 h-index
4 publications
228 citations
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Repository · 9 publications · 349 citations
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2 publications · 185 citations
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3 publications · 112 citations
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3 publications · 93 citations
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2 publications · 187 citations
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