What is a support vector machine?

Noble WS

doi:10.1038/nbt1206-1565

Abstract

Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. But, what exactly are SVMs and how do they work? And what are their most promising applications in the life sciences?

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Read article at publisher's site: https://doi.org/10.1038/nbt1206-1565

References

Articles referenced by this article (8)

Boser, B.E., Guyon, I.M. & Vapnik, V.N. A training algorithm for optimal margin classifiers. in 5th Annual ACM Workshop on COLT (ed. Haussler, D.) 144–152 (ACM Press, Pittsburgh, PA, 1992).
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
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Noble, W.S. Support vector machine applications in computational biology. in Kernel Methods in Computational Biology (eds. Schoelkopf, B., Tsuda, K. & Vert, J.-P.) 71–92 (MIT Press, Cambridge, MA, 2004).
Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002).
Vapnik, V. & Lerner, A. Pattern recognition using generalized portrait method. Autom. Remote Control 24, 774–780, 1963.
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Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov JP, Poggio T, Gerald W, Loda M, Lander ES, Golub TR
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Bordes, A., Ertekin, S., Weston, J. & Bottou, L. Fast kernel classifiers with online and active learning. J. Mach. Learn. Res. 6, 1579–1619 (2005).
Support vector machine classification and validation of cancer tissue samples using microarray expression data.
Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D
Bioinformatics, (10):906-914 2000
MED: 11120680

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What is a support vector machine?

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Authors

Abstract

Full text links

References

Boser, B.E., Guyon, I.M. & Vapnik, V.N. A training algorithm for optimal margin classifiers. in 5th Annual ACM Workshop on COLT (ed. Haussler, D.) 144–152 (ACM Press, Pittsburgh, PA, 1992).

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Noble, W.S. Support vector machine applications in computational biology. in Kernel Methods in Computational Biology (eds. Schoelkopf, B., Tsuda, K. & Vert, J.-P.) 71–92 (MIT Press, Cambridge, MA, 2004).

Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002).

Vapnik, V. & Lerner, A. Pattern recognition using generalized portrait method. Autom. Remote Control 24, 774–780, 1963.

Multiclass cancer diagnosis using tumor gene expression signatures.

Bordes, A., Ertekin, S., Weston, J. & Bottou, L. Fast kernel classifiers with online and active learning. J. Mach. Learn. Res. 6, 1579–1619 (2005).

Support vector machine classification and validation of cancer tissue samples using microarray expression data.

Citations & impact

Impact metrics

Citations of article over time

Alternative metrics

Article citations

TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.

Molecular Indicator for Distinguishing Multi-drug-Resistant Tuberculosis from Drug Sensitivity Tuberculosis and Potential Medications for Treatment.

Interpretable Clinical Decision-Making Application for Etiological Diagnosis of Ventricular Tachycardia Based on Machine Learning.

Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing-Assisted Intelligent Systematic Literature Review System.

Quantitative physics-physiology relationship modeling of human emotional response to Shu music.

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Search life-sciences literature (45,103,589 articles, preprints and more)

What is a support vector machine?

Author information

Affiliations

Authors

Abstract

Full text links

References

Boser, B.E., Guyon, I.M. & Vapnik, V.N. A training algorithm for optimal margin classifiers. in 5th Annual ACM Workshop on COLT (ed. Haussler, D.) 144–152 (ACM Press, Pittsburgh, PA, 1992).

Noble, W.S. Support vector machine applications in computational biology. in Kernel Methods in Computational Biology (eds. Schoelkopf, B., Tsuda, K. & Vert, J.-P.) 71–92 (MIT Press, Cambridge, MA, 2004).

Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002).

Vapnik, V. & Lerner, A. Pattern recognition using generalized portrait method. Autom. Remote Control 24, 774–780, 1963.

Bordes, A., Ertekin, S., Weston, J. & Bottou, L. Fast kernel classifiers with online and active learning. J. Mach. Learn. Res. 6, 1579–1619 (2005).

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