Please use this identifier to cite or link to this item:
|Title:||Improvements to Platt's SMO algorithm for SVM classifier design|
|Authors:||Keerthi, S.S. |
|Citation:||Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K. (2001-03). Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation 13 (3) : 637-649. ScholarBank@NUS Repository. https://doi.org/10.1162/089976601300014493|
|Abstract:||This article points out an important source of inefficiency in Platt's sequential minimal optimization (SMO) algorithm 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. These modified algorithms perform significantly faster than the original SMO on all benchmark data sets tried.|
|Source Title:||Neural Computation|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 21, 2019
WEB OF SCIENCETM
checked on Mar 12, 2019
checked on Dec 15, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.