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Sequential minimal optimization

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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Class
Optimization algorithm for training support vector machines
Worst-case performance
O(n³)

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Optimization problem

Algorithm

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Sequential minimal optimization

Nodes22
Edges21
Triples2
Avg. degree1.91
Density0.090909
Components1

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Sequential minimal optimization

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Class · 1
Sequential minimal optimization → Optimization algorithm for training support vector machines
Worst-case performance · 1
Sequential minimal optimization → O(n³)

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Important terminology

algorithm problem smo optimization training displaystyle qp alpha multipliers svm vector machines lagrange conditions methods support solving quadratic programming 1998

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SubjectPredicateObjectConfidenceSrc
Sequential minimal optimizationClassOptimization algorithm for training support vector machines1.00infobox
Sequential minimal optimizationWorst-case performanceO(n³)1.00infobox

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