The DOI matches its Crossref record; 47 of the 48 references it cites were found in a registry or an index.
Gene Selection for Cancer Classification using Support Vector Machines
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“The data structure is captured by this efficient sparse coding model, which also finds correlations between different input vectors [92].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [92]
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Self-citationCluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Wrappers for feature subset selection
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Research works printed but not linked: 2
- Ghigna, C., Moroni, M., Porta, C., Riva, I., & Biamonti, G. (1998). Altered expression of heterogeneous nuclear ribonucleoproteins and SR factors in human. Cancer Research, 58, 5818-5824.
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Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
A review of feature selection techniques in bioinformatics
Saeys, Y., Inza, I., & Larrañaga, P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm344 · Cited by 5,389
A survey on feature selection methods
Chandrashekar, G., & Sahin, F. (2014) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.11.024 · Cited by 5,189
What is a support vector machine?
Noble, W. S. (2006) · Nature Biotechnology · doi:10.1038/nbt1206-1565 · Cited by 4,450
Metadata
- Authors
- Guyon, I., Weston, J., Barnhill, S., & Vapnik, V.
- Affiliations
- Georgia Institute of Technology (US)
- AT&T (United States) (US)
- Corresponding author
- Guyon, Isabelle
- Vapnik, Vladimir
- ORCID
- Guyon, Isabelle
- Venue
- Machine Learning
- Published
- 2002-01
- Volume
- 46
- Issue
- 1-3
- Pages
- 389-422
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2143426320
- Cited by
- 9,954
- References
- 48
- Citations by year
- 2026: 553 · 2025: 676 · 2024: 564 · 2023: 586 · 2022: 576 · 2021: 695 · 2020: 607 · 2019: 571 · 2018: 488 · 2017: 478 · 2016: 408 · 2015: 444 · 2014: 400 · 2013: 400 · 2012: 359
- FWCI
- 38.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- Delft Research Centres
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- gene selection, cancer classification, DNA microarrays, gene expression data, recursive feature elimination, support vector machine, leukemia, colon cancer, gene redundancy, genetic diagnosis, drug discovery
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