Sequential minimal optimization, or SMO, is an algorithm for solving the quadratic programming problem that arises when training a support-vector machine. It was invented by John Platt in 1998 at Microsoft Research, and it is widely used for training support vector machines, implemented in the popular LIBSVM tool, because earlier SVM training methods required expensive third-party quadratic programming solvers.
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Source Sequential Minimal Optimization (Wikipedia)
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1. Sequential Minimal Optimization (Wikipedia)
Wikipedia lead/infobox
time=O(n³)
In Field: Machine Learning, Lead sentence
Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM).
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