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ISSN
0899-7667
E-ISSN
1530-888X
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Neural Computation

February 1, 2003, Vol. 15, No. 2, Pages 487-507
(doi: 10.1162/089976603762553013)
© 2002 Massachusetts Institute of Technology
SMO Algorithm for Least-Squares SVM Formulations
Article PDF (137.73 KB)
Abstract

This article extends the well-known SMO algorithm of support vector machines (SVMs) to least-squares SVM formulations that include LS-SVM classification, kernel ridge regression, and a particular form of regularized kernel Fisher discriminant. The algorithm is shown to be asymptotically convergent. It is also extremely easy to implement. Computational experiments show that the algorithm is fast and scales efficiently (quadratically) as a function of the number of examples.