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A Bahadur representation of the linear support vector machine

Authors
Koo, Ja-YongLee, YoonkyungKim, YuwonPark, Changyi
Issue Date
7월-2008
Publisher
MICROTOME PUBL
Keywords
asymptotic normality; Bahadur representation; classification; convexity lemma; Radon transform
Citation
JOURNAL OF MACHINE LEARNING RESEARCH, v.9, pp.1343 - 1368
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF MACHINE LEARNING RESEARCH
Volume
9
Start Page
1343
End Page
1368
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/123281
ISSN
1532-4435
Abstract
The support vector machine has been successful in a variety of applications. Also on the theoretical front, statistical properties of the support vector machine have been studied quite extensively with a particular attention to its Bayes risk consistency under some conditions. In this paper, we study somewhat basic statistical properties of the support vector machine yet to be investigated, namely the asymptotic behavior of the coefficients of the linear support vector machine. A Bahadur type representation of the coefficients is established under appropriate conditions, and their asymptotic normality and statistical variability are derived on the basis of the representation. These asymptotic results do not only help further our understanding of the support vector machine, but also they can be useful for related statistical inferences.
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정경대학 (통계학과)
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