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

Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2002.1031955

Title:	Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
Authors:	Keerthi, S.S.
Keywords:	Hyperparameter tuning Support vector machines (SVMs)
Issue Date:	Sep-2002
Citation:	Keerthi, 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
Abstract:	Various 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.
Source Title:	IEEE Transactions on Neural Networks
URI:	http://scholarbank.nus.edu.sg/handle/10635/85091
ISSN:	10459227
DOI:	10.1109/TNN.2002.1031955
Appears in Collections:	Staff Publications

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