The Strength of Weak Learnability
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A combination of many weak learners yields a more powerful predictor than a single strong learner on its own.
“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
Section 3.1, “The hypothesis boosting mechanism” (page unavailable), shows that combining three hypotheses reduces error relative to the original hypothesis; Section 3.2 describes recursively boosting weaker hypotheses. The paper does not claim that the resulting predictor outperforms a single strong learner, so the stated comparator is unsupported. (LLM, conf=0.90)
Experimental — accuracy not yet measuredBoosting is originally described in Schapire (1990) and Freund (1995) for classification problems to improve the performance of a set of weak learners.
“Boosting is originally described inSchapire (1990) andFreund (1995) for classification problems to improve the performance of a set of weak learners.”
From Empirical Asset Pricing via Machine Learning (2020) · cites this paper as Schapire (1990)
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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