Sequential minimal optimization: Difference between revisions

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'''Sequential minimal optimization''' ('''SMO''') is an algorithm for solving the [[quadratic programming]] (QP) problem that arises during the training of [[support -vector machine]]s (SVM). It was invented by [[John Platt (Principalcomputer Researcherscientist)|John Platt]] in 1998 at [[Microsoft Research]].<ref name = "Platt">{{CitationCite paper

| 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>{{cite web |first=Luca |last=Zanni (|date=2006). ''[|url=http://jmlr.csail.mit.edu/papers/volume7/zanni06a/zanni06a.pdf |title=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 QP solvers.<ref>{{Citecite thesis

| last = Rifkin | first = Ryan

| year = 2002

| hdl=1721.1/17549

| title = Everything Old is New Again: a Fresh Look at Historical Approaches in Machine Learning

| type = Ph.D. Thesis |publisher=Massachusetts Institute of Technology

| pages = 18

| mode=cs2

}}</ref>