SMO algorithm for least squares SVM
Keerthi, S.S.; Shevade, S.K.
Neural Networks, 2003. Proceedings of the International Joint Conference on
Volume 3, Issue , 20-24 July 2003 Page(s): 2088 - 2093 vol.3
Digital Object Identifier   10.1109/IJCNN.2003.1223730
Summary: This paper extends the well-known SMO (Sequential Minimal Optimization) algorithm of Support Vector Machines (SVMs) to Least Squares SVM formulation. The algorithm is 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.

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