The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Statistical Learning Theory
Abstract
A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Self-citation
Cited by
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
“This method has been suggested by Wahba et al. [1995] (in the context of numerical weather prediction) and by Gibbs and MacKay [1997] (in the context of general GP regression).”
Metadata
- Authors
- Wu, Y.
- Venue
- Technometrics
- Publisher
- Informa UK Limited
- Published
- 1999-11
- Volume
- 41
- Issue
- 4
- Pages
- 377-378
- ISSN
- 0040-1706 (ISSN-L)
- 1537-2723
- OpenAlex
- W2148603752
- Cited by
- 27,221
- References
- 1
- FWCI
- 371.91
- Type
- journal article
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
