Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural ComputationPublished 1 March 2001
S. Sathiya Keerthi, Shirish Shevade, Chiranjib Bhattacharyya, K. R. K. Murthy
Citations1,817
SJR quartileQ1
SJR score0.83
SNIP1.45
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TL;DR
Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO that perform significantly faster than the original SMO on all benchmark data sets tried.
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.
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