The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Comparing support vector machines to PLS for spectral regression applications
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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
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Savitzky, A., & Golay, M. J. E. (1964) · Analytical Chemistry · doi:10.1021/ac60214a047 · Cited by 21,904
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
The Nature of Statistical Learning Theory
Sain, S. R. (1996) · Technometrics · doi:10.1080/00401706.1996.10484565 · Cited by 5,434
Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra
Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989) · Applied Spectroscopy · doi:10.1366/0003702894202201 · Cited by 3,867
Least Squares Support Vector Machines
Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J. (2002) · WORLD SCIENTIFIC eBooks · doi:10.1142/5089 · Cited by 3,642
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,308
Cited by
Applications of Support Vector Machines in Chemistry
Ivanciuc, O. (2007) · Reviews in Computational Chemistry · doi:10.1002/9780470116449.ch6 · Cited by 384
Shi, T., Chen, Y., Liu, Y., & Wu, G. (2014) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2013.11.059 · Cited by 377
Support vector machines and its applications in chemistry
Li, H., Liang, Y., & Xu, Q. (2009) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2008.10.007 · Cited by 337
Üstün, B., Melssen, W. J., & Buydens, L. M. C. (2006) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2005.09.003 · Cited by 305
Self-citationPark, Y., Cho, K. H., Park, J., Cha, S. M., & Kim, J. H. (2015) · Science of The Total Environment · doi:10.1016/j.scitotenv.2014.09.005 · Cited by 272
Metadata
- Authors
- Thissen, U., Pepers, M., Üstün, B., Melssen, W. J., & Buydens, L. M. C.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2004-10
- Volume
- 73
- Issue
- 2
- Pages
- 169-179
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W2029372760
- Cited by
- 372
- References
- 26
- FWCI
- 10.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Thissen, U., Pepers, M., Üstün, B., Melssen, W. J., & Buydens, L. M. C. (2004). Comparing support vector machines to PLS for spectral regression applications. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2004.01.002
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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.
