Please use this identifier to cite or link to this item:
|Title:||Improvements to the SMO algorithm for SVM regression||Authors:||Shevade, S.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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 25, 2020
WEB OF SCIENCETM
checked on Feb 17, 2020
checked on Feb 18, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.