Improvements to Platt's SMO algorithm for SVM classifier design
Improvements to Platt's SMO algorithm for SVM classifier design
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DOI：
10.1162/089976601300014493
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发表时间：
2001-03-01
影响因子：
2.9
通讯作者：
Murthy, KRK
中科院分区：
文献类型：
--
作者：
Keerthi, SS;Shevade, SK;Murthy, KRK
本文指出了Platt的序列最小优化(SMO)算法由于使用单一阈值而导致效率低下的一个重要来源。利用对偶问题的KKT条件的线索，利用两个阈值参数来推导SMO的修正。这些改进的算法在所有测试的基准数据集上的执行速度明显快于原始的SMO。
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.