SMO algorithm for least-squares SVM formulations

Authors:
S. S. Keerthi;S. K. Shevade
Affiliations:
Department of Mechanical Engineering, National University of Singapore, Singapore;Genome Institute of Singapore, National University of Singapore, Singapore
Venue:
Neural Computation
Year:
2003

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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.