The DOI matches its Crossref record.
Boosting a Weak Learning Algorithm by Majority
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In the citing paper
“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 Freund (1995)
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Pattern discrimination method with a boosting approach using hierarchical neural trees
Okamoto, M., Shima, K., Matsubara, Y., & Tsuji, T. (2008) · Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · doi:10.1243/09596518jsce563 · Cited by 5
Metadata
- Authors
- Freund, Y.
- Affiliations
- AT&T (United States) (US)
- Corresponding author
- Freund, Y.
- ORCID
- Freund, Y.
- Venue
- Information and Computation
- Publisher
- Elsevier BV
- Published
- 1995-09
- Volume
- 121
- Issue
- 2
- Pages
- 256-285
- ISSN
- 0890-5401 (ISSN-L)
- OpenAlex
- W2070534370
- Cited by
- 1,674
- References
- 0
- Citations by year
- 2026: 30 · 2025: 54 · 2024: 64 · 2023: 70 · 2022: 68 · 2021: 82 · 2020: 72 · 2019: 79 · 2018: 55 · 2017: 57 · 2016: 55 · 2015: 59 · 2014: 56 · 2013: 68 · 2012: 67
- FWCI
- 15.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Information and Computation (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Complexity and Algorithms in Graphs
- Physical Sciences › Computer Science › Computational Theory and Mathematics › semigroups and automata theory
- Keywords
- boosting, weak learning, majority voting, PAC learning, learnability, threshold circuits, power of representation, generalization bounds
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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.
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