Margin-like quantities and generalized approximate cross validation for support vector machines

Grace Wahba; Yi Lin; Hao Zhang

Margin-like quantities and generalized approximate cross validation for support vector machines

Grace Wahba, Yi Lin, Hao Zhang

Research output: Contribution to conference › Paper › peer-review

Abstract

We examine Support Vector Machines from the point of view of solutions to variational problems in a reproducing kernel Hilbert space. We discuss the Generalized Comparative Kullback-Leibler Distance as a target for choosing tuning parameters in SVM's, and we propose that the Generalized Approximate Cross Validation estimate of them is a reasonable proxy for this target. We indicate an interesting relationship between the GACV and the SVM margin.

Original language	English (US)
Pages	12-20
Number of pages	9
State	Published - 1999
Event	Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA Duration: Aug 23 1999 → Aug 25 1999

Conference

Conference	Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99)
City	Madison, WI, USA
Period	8/23/99 → 8/25/99

ASJC Scopus subject areas

Signal Processing
Software
Electrical and Electronic Engineering

Cite this

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title = "Margin-like quantities and generalized approximate cross validation for support vector machines",

abstract = "We examine Support Vector Machines from the point of view of solutions to variational problems in a reproducing kernel Hilbert space. We discuss the Generalized Comparative Kullback-Leibler Distance as a target for choosing tuning parameters in SVM's, and we propose that the Generalized Approximate Cross Validation estimate of them is a reasonable proxy for this target. We indicate an interesting relationship between the GACV and the SVM margin.",

author = "Grace Wahba and Yi Lin and Hao Zhang",

year = "1999",

language = "English (US)",

pages = "12--20",

note = "Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) ; Conference date: 23-08-1999 Through 25-08-1999",

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T1 - Margin-like quantities and generalized approximate cross validation for support vector machines

AU - Wahba, Grace

AU - Lin, Yi

AU - Zhang, Hao

PY - 1999

Y1 - 1999

N2 - We examine Support Vector Machines from the point of view of solutions to variational problems in a reproducing kernel Hilbert space. We discuss the Generalized Comparative Kullback-Leibler Distance as a target for choosing tuning parameters in SVM's, and we propose that the Generalized Approximate Cross Validation estimate of them is a reasonable proxy for this target. We indicate an interesting relationship between the GACV and the SVM margin.

AB - We examine Support Vector Machines from the point of view of solutions to variational problems in a reproducing kernel Hilbert space. We discuss the Generalized Comparative Kullback-Leibler Distance as a target for choosing tuning parameters in SVM's, and we propose that the Generalized Approximate Cross Validation estimate of them is a reasonable proxy for this target. We indicate an interesting relationship between the GACV and the SVM margin.

UR - http://www.scopus.com/inward/record.url?scp=0033350009&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033350009&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0033350009

SP - 12

EP - 20

T2 - Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99)

Y2 - 23 August 1999 through 25 August 1999

ER -

Margin-like quantities and generalized approximate cross validation for support vector machines

Abstract

Conference

ASJC Scopus subject areas

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