Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms.

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Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. Abstract Text:

S S Keerthi,

The paper discusses implementation issues related to the tuning of the hyperparameters of a support vector machine (SVM) with L/sub 2/ soft margin, for which the radius/margin bound is taken as the index to be minimized, and iterative techniques are employed for computing radius and margin. The implementation is shown to be feasible and efficient, even for large problems having more than 10000 support vectors.

Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. Publishing Authors By Initials

SS Keerthi,

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Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: IEEE transactions on neural networks / a publicati

VOLUME: 13

Page Numbers: 1225-9

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ISSN: 1045-9227

DAY: 4

MONTH: 02

YEAR: 2002

Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. Information

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LANGUAGE: eng

NlmUniqueID: 101211035

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Grant and Affiliation Information for Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms.

AFFILIATION: Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore.

Country: United States

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MEDLINETA: IEEE Trans Neural Netw

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