The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
A tutorial on support vector machine-based methods for classification problems in chemometrics
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Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
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
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
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
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
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
Cited by
Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry
Verbeeck, N., Caprioli, R. M., & Van de Plas, R. (2020) · Mass Spectrometry Reviews · doi:10.1002/mas.21602 · Cited by 281
Self-citationIn vivo evidence of neurophysiological maturation of the human adolescent striatum
Larsen, B., & Luna, B. (2015) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2014.12.003 · Cited by 78
Metadata
- Authors
- Luts, J., Ojeda, F., Van de Plas, R., De Moor, B., Van Huffel, S., & Suykens, J. A. K.
- Venue
- Analytica Chimica Acta
- Publisher
- Elsevier BV
- Published
- 2010-04
- Volume
- 665
- Issue
- 2
- Pages
- 129-145
- ISSN
- 0003-2670 (ISSN-L)
- OpenAlex
- W2081480383
- Cited by
- 326
- References
- 89
- FWCI
- 15.14
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A tutorial on support vector machine-based methods for classification problems in chemometrics. https://app.cochat.ai/papers/10.1016/j.aca.2010.03.030
- The paper
- Luts, J., Ojeda, F., Van de Plas, R., De Moor, B., Van Huffel, S., & Suykens, J. A. K. (2010). A tutorial on support vector machine-based methods for classification problems in chemometrics. Analytica Chimica Acta. https://doi.org/10.1016/j.aca.2010.03.030
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.
