The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Learning from noisy examples
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Citations
Cites
Probability Inequalities for sums of Bounded Random Variables
Hoeffding, W. (1994) · Springer Series in Statistics · doi:10.1007/978-1-4612-0865-5_26 · Cited by 6,666
Probability Inequalities for Sums of Bounded Random Variables
Hoeffding, W. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500830 · Cited by 4,852
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
Learning regular sets from queries and counterexamples
Angluin, D. (1987) · Information and Computation · doi:10.1016/0890-5401(87)90052-6 · Cited by 2,114
Self-citationIncremental learning from noisy data
Schlimmer, J. C., & Granger, R. H. (1986) · Machine Learning · doi:10.1007/bf00116895 · Cited by 653
On the learnability of Boolean formulae
Kearns, M., Li, M., Pitt, L., & Valiant, L. (1987) · Proceedings of the nineteenth annual ACM conference on Theory of computing - STOC '87 · doi:10.1145/28395.28426 · Cited by 284
Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. (1986) · Proceedings of the eighteenth annual ACM symposium on Theory of computing - STOC '86 · doi:10.1145/12130.12158 · Cited by 159
Learning in the presence of malicious errors
Kearns, M. J., & Li, M. (1988) · doi:10.1145/62212.62238 · Cited by 127
Learning from good data and bad
Laird, P. D. (1987) · Yale University eBooks · Cited by 19
Self-citationValiant, L. G. (2026) · Foundations of Computation and Machine Learning · doi:10.1145/3749740.3749746
Cited by
A brief introduction to weakly supervised learning
Zhou, Z.-H. (2018) · National Science Review · doi:10.1093/nsr/nwx106 · Cited by 1,899
Angluin, D. (1988) · Machine Learning · doi:10.1007/bf00116828 · Cited by 1,905
Self-citationLearnability and the Vapnik-Chervonenkis dimension
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1989) · Journal of the ACM · doi:10.1145/76359.76371 · Cited by 1,839
Classification in the Presence of Label Noise: A Survey
Frenay, B., & Verleysen, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2292894 · Cited by 1,777
Confident Learning: Estimating Uncertainty in Dataset Labels
Northcutt, C., Jiang, L., & Chuang, I. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12125 · Cited by 795
Metadata
- Authors
- Angluin, D., & Laird, P.
- Venue
- Machine Learning
- Published
- 1988-04
- Volume
- 2
- Issue
- 4
- Pages
- 343-370
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2107189314
- Cited by
- 827
- References
- 10
- FWCI
- 15.74
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
