Class | Optimization algorithm for training support vector machines |
---|---|
Worst-case performance | O(n³) |
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.[1] SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.[2][3] The publication of the SMO algorithm in 1998 has generated a lot of excitement in the SVM community, as previously available methods for SVM training were much more complex and required expensive third-party QP solvers.[4]