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Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). It was invented by John Platt in 1998 at Microsoft Research. SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool. The publication of…
Works, Related work & Optimization problem
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algorithm problem smo optimization training displaystyle qp alpha multipliers svm vector machines lagrange conditions methods support solving quadratic programming 1998
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Sequential minimal optimization | Class | Optimization algorithm for training support vector machines | 1.00 | infobox |
| Sequential minimal optimization | Worst-case performance | O(n³) | 1.00 | infobox |
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