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Cited by 741

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Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data

Lee, Yoonkyung, Lin, Yi, Wahba, Grace·2004·Journal of the American Statistical Association

Abstract

Two-category support vector machines (SVM) have been very popular in the machine learning community for classification problems. Solving multicategory problems by a series of binary classifiers is quite common in the SVM paradigm; however, this approach may fail under various circumstances. We propose the multicategory support vector machine (MSVM), which extends the binary SVM to the multicategory case and has good theoretical properties. The proposed method provides a unifying framework when there are either equal or unequal misclassification costs. As a tuning criterion for the MSVM, an approximate leave-one-out cross-validation function, called Generalized Approximate Cross Validation, is derived, analogous to the binary case. The effectiveness of the MSVM is demonstrated through the applications to cancer classification using microarray data and cloud classification with satellite radiance profiles.

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Metadata

Authors
Lee, Y., Lin, Y., & Wahba, G.
Venue
Journal of the American Statistical Association
Published
2004-03
Volume
99
Issue
465
Pages
67-81
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
OpenAlex
W2116444583
Cited by
741
References
6
FWCI
33.89
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data. https://app.cochat.ai/papers/10.1198/016214504000000098
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Lee, Y., Lin, Y., & Wahba, G. (2004). Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data. Journal of the American Statistical Association. https://doi.org/10.1198/016214504000000098

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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.