Efficient SVM Regression Training with SMO
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A note on the decomposition methods for support vector regression
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Structural and Multidisciplinary Optimization
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Artificial Intelligence in Medicine
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Machine Translation
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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