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Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2002.1031955
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dc.titleEfficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
dc.contributor.authorKeerthi, S.S.
dc.date.accessioned2014-10-07T09:03:53Z
dc.date.available2014-10-07T09:03:53Z
dc.date.issued2002-09
dc.identifier.citationKeerthi, S.S. (2002-09). Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE Transactions on Neural Networks 13 (5) : 1225-1229. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2002.1031955
dc.identifier.issn10459227
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/85091
dc.description.abstractVarious implementation issues associated with the tuning of hyperparameters for the SVM l2 soft margin problem was studied, by minimizing the radius/margin criterion and employing iterative techniques for obtaining radius and margin. The experiments indicated the usefulness of the radius/margin criterion and the associated implementation. The extension of the implementation to the simultaneous tuning of many other hyperparameters was also discussed.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TNN.2002.1031955
dc.sourceScopus
dc.subjectHyperparameter tuning
dc.subjectSupport vector machines (SVMs)
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1109/TNN.2002.1031955
dc.description.sourcetitleIEEE Transactions on Neural Networks
dc.description.volume13
dc.description.issue5
dc.description.page1225-1229
dc.description.codenITNNE
dc.identifier.isiut000177992800021
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