The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Support vector machines and its applications in chemistry
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Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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
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
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,429
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
FISHER, R. A. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 15,227
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
John Lu, Z. Q. (2010) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,390
The Nature of Statistical Learning Theory
Vapnik, V. N. (2000) · Springer eBooks · doi:10.1007/978-1-4757-3264-1 · Cited by 10,696
Cited by
Machine learning in manufacturing: Advantages, challenges, and applications
Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016) · Production & Manufacturing Research · doi:10.1080/21693277.2016.1192517 · Cited by 1,319
Deep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
Marvuglia, A., Kanevski, M., & Benetto, E. (2015) · Environment International · doi:10.1016/j.envint.2015.05.011 · Cited by 41
Metadata
- Authors
- Li, H., Liang, Y., & Xu, Q.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2009-02
- Volume
- 95
- Issue
- 2
- Pages
- 188-198
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W2021211279
- Cited by
- 337
- References
- 70
- FWCI
- 26.50
- Type
- journal article
- 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: Support vector machines and its applications in chemistry. https://app.cochat.ai/papers/10.1016/j.chemolab.2008.10.007
- The paper
- Li, H., Liang, Y., & Xu, Q. (2009). Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2008.10.007
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.
