The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Kernel Matching Pursuit
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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
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Matching pursuits with time-frequency dictionaries
Mallat, S. G., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,112
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,789
Pati, Y. C., Rezaiifar, R., & Krishnaprasad, P. S. (2002) · Proceedings of 27th Asilomar Conference on Signals, Systems and Computers · doi:10.1109/acssc.1993.342465 · Cited by 4,323
Orthogonal least squares learning algorithm for radial basis function networks
Chen, S., Cowan, C. F. N., & Grant, P. M. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.80341 · Cited by 3,362
Boosting the margin: a new explanation for the effectiveness of voting methods
Bartlett, P., Freund, Y., Lee, W. S., & Schapire, R. E. (1998) · The Annals of Statistics · doi:10.1214/aos/1024691352 · Cited by 2,306
Cited by
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
Kim, K. I., & Kwon, Y. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.25 · Cited by 956
Building Support Vector Machines with Reduced Classifier Complexity
Keerthi, S. S., Chapelle, O., & DeCoste, D. (2006) · MPG.PuRe (Max Planck Society) · doi:10.5555/1248547.1248602 · Cited by 263
Metadata
- Authors
- Vincent, P., & Bengio, Y.
- Venue
- Machine Learning
- Published
- 2002-07
- Volume
- 48
- Issue
- 1-3
- Pages
- 165-187
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2129809168
- Cited by
- 317
- References
- 35
- FWCI
- 13.24
- 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.
Statistics and arithmetic are recomputed, not judged.
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Kernel Matching Pursuit. https://app.cochat.ai/papers/10.1023/a:1013955821559
- The paper
- Vincent, P., & Bengio, Y. (2002). Kernel Matching Pursuit. Machine Learning. https://doi.org/10.1023/a%3A1013955821559
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.
