Please use this identifier to cite or link to this item: https://doi.org/10.1162/089976601300014493
Title: Improvements to Platt's SMO algorithm for SVM classifier design
Authors: Keerthi, S.S. 
Shevade, S.K.
Bhattacharyya, C.
Murthy, K.R.K.
Issue Date: Mar-2001
Source: 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
URI: http://scholarbank.nus.edu.sg/handle/10635/60498
ISSN: 08997667
DOI: 10.1162/089976601300014493
Appears in Collections:Staff Publications

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