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The strength of weak learnability
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“The general principle behind boosting is that a combination of many weak learners yields a more powerful predictor than a single strong learner on its own (Freund & Schapire, 1997;Schapire, 1990).”
From Customer price sensitivities in competitive insurance markets (2022) · cites this paper as Schapire, 1990
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“Boosting is originally described inSchapire (1990) andFreund (1995) for classification problems to improve the performance of a set of weak learners.”
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Metadata
- Authors
- Schapire, R. E.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Schapire, Robert E.
- ORCID
- Schapire, Robert E.
- Venue
- Machine Learning
- Published
- 1990-06
- Volume
- 5
- Issue
- 2
- Pages
- 197-227
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2093717447
- Cited by
- 4,600
- References
- 22
- Citations by year
- 2026: 106 · 2025: 211 · 2024: 195 · 2023: 226 · 2022: 226 · 2021: 255 · 2020: 248 · 2019: 257 · 2018: 195 · 2017: 173 · 2016: 173 · 2015: 188 · 2014: 205 · 2013: 197 · 2012: 187
- FWCI
- 8.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Funders
- Siemens USA
- Defense Advanced Research Projects Agency
- Army Research Office (DAAL03-86-K-0171)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- PAC learning, boosting, distribution-free learning
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