Sequential minimal optimization: Difference between revisions

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'''Sequential minimal optimization''' ('''SMO''') is an algorithm for solving the [[optimizationquadratic problemprogramming]] which(QP) problem that arises during the training of [[support vector machine]]s. It was invented by [[John Platt (Principal Researcher)|John Platt]] in 1998 at [[Microsoft Research]].<ref>{{Citation

| last = Platt | first = John

| year = 1998

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|journal=ACM Transactions on Intelligent Systems and Technology

|volume=2 |issue=3 |year=2011

}}</ref><ref>Luca Zanni (2006). ''[http://jmlr.csail.mit.edu/papers/volume7/zanni06a/zanni06a.pdf Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems]''.</ref> 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 [[Quadratic programming|QP]] solvers.<ref>{{Citation

| last = Rifkin | first = Ryan

| year = 2002