Improvements to the SMO algorithm for SVM regression

Please use this identifier to cite or link to this item: https://doi.org/10.1109/72.870050

Title:	Improvements to the SMO algorithm for SVM regression
Authors:	Shevade, S.K. Keerthi, S.S. Bhattacharyya, C. Murthy, K.R.K.
Issue Date:	Sep-2000
Citation:	Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K. (2000-09). Improvements to the SMO algorithm for SVM regression. IEEE Transactions on Neural Networks 11 (5) : 1188-1193. ScholarBank@NUS Repository. https://doi.org/10.1109/72.870050
Abstract:	This paper points out an important source of inefficiency in Smola and Scholkopf's sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression. These modified algorithms perform significantly faster than the original SMO on the datasets tried.
Source Title:	IEEE Transactions on Neural Networks
URI:	http://scholarbank.nus.edu.sg/handle/10635/58383
ISSN:	10459227
DOI:	10.1109/72.870050
Appears in Collections:	Staff Publications

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