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Sequential minimal optimization

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In mathematical optimization and machine learning, sequential minimal optimization (SMO) is an algorithm for solving large quadratic programming (QP) optimization problems, widely used for the training of support vector machines. First developed by John C. Platt in 1999,[1] SMO breaks up large QP problems into a series of smallest possible QP problems, which are then solved analytically.

References

  1. ^ Platt, J.C. (1999), Fast training of support vector machines using sequential minimal optimization
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