Shevade, S.K. ; Keerthi, S.S. ; Bhattacharyya, C. ; Murthy, K.R.K. (2000) Improvements to the SMO algorithm for SVM regression IEEE Transactions on Neural Networks, 11 (5). pp. 1188-1193. ISSN 10459227
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Official URL: http://doi.org/10.1109/72.870050
Related URL: http://dx.doi.org/10.1109/72.870050
Abstract
This paper points out an important source of inefficiency in Smola and Scholkopf's (1998) sequential minimal optimization (SMO) algorithm for support vector machine regression that is caused by the use of a single threshold value. Using clues from the Karush-Kuhn-Tucker 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.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to IEEE |
| ID Code: | 127672 |
| Deposited On: | 13 Oct 2022 10:59 |
| Last Modified: | 13 Oct 2022 10:59 |
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