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]
^Platt, John (1998). "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines" (PDF). CiteSeerX 10.1.1.43.4376.
^Chang, Chih-Chung; Lin, Chih-Jen (2011). "LIBSVM: A library for support vector machines". ACM Transactions on Intelligent Systems and Technology. 2 (3). doi:10.1145/1961189.1961199. S2CID 961425.
^Zanni, Luca (2006). "Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems" (PDF).
^Rifkin, Ryan (2002). Everything Old is New Again: a Fresh Look at Historical Approaches in Machine Learning (Ph.D. Thesis). Massachusetts Institute of Technology. p. 18. hdl:1721.1/17549.
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