The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Introduction to Statistical Learning Theory
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Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,522
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 20,825
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,031
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
Weak Convergence and Empirical Processes
van der Vaart, A. W., & Wellner, J. A. (1996) · Springer Series in Statistics · doi:10.1007/978-1-4757-2545-2 · Cited by 5,014
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
A Probabilistic Theory of Pattern Recognition
Devroye, L., Györfi, L., & Lugosi, G. (1996) · Stochastic Modelling and Applied Probability · doi:10.1007/978-1-4612-0711-5 · Cited by 3,274
Self-citation
Cited by
Fisher, A., Rudin, C., & Dominici, F. (2018) · arXiv · doi:10.48550/arxiv.1801.01489 · Cited by 1,165
Philosophy and the practice of Bayesian statistics
Gelman, A., & Shalizi, C. R. (2013) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.2011.02037.x · Cited by 825
(2011) · Cited by 675
Testing the manifold hypothesis
Fefferman, C., Mitter, S., & Narayanan, H. (2016) · Journal of the American Mathematical Society · doi:10.1090/jams/852 · Cited by 430
Deep Learning Is Singular, and That’s Good
Wei, S., Murfet, D., Gong, M., Li, H., Gell-Redman, J., & Quella, T. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3167409 · Cited by 26
Metadata
- Authors
- Bousquet, O., Boucheron, S., & Lugosi, G.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2004
- Pages
- 169-207
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2111296615
- Cited by
- 503
- References
- 80
- FWCI
- 4.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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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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- CoChat. (2026). Citation check: Introduction to Statistical Learning Theory. https://app.cochat.ai/papers/10.1007/978-3-540-28650-9_8
- The paper
- Bousquet, O., Boucheron, S., & Lugosi, G. (2004). Introduction to Statistical Learning Theory. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-28650-9_8
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.
