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.
Facts
Time Complexity
Time Complexity (category) Connections
In Field
Source Sequential Minimal Optimization (Wikipedia)
Uses Design Technique
Source Sequential Minimal Optimization (Wikipedia)
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Developed By
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Sources
1. Sequential Minimal Optimization (Wikipedia)
Infobox: time
O(n³)
Uses Design Technique: Heuristics, Article body
Although this algorithm is guaranteed to converge, heuristics are used to choose the pair of multipliers so as to accelerate the rate of convergence.
In Group: Supervised and Online Learning Algorithms, Introduction
Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM).
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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