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|Title:||SMO Algorithm for Least Squares SVM||Authors:||Keerthi, S.S.
|Issue Date:||2003||Citation:||Keerthi, S.S.,Shevade, S.K. (2003). SMO Algorithm for Least Squares SVM. Proceedings of the International Joint Conference on Neural Networks 3 : 2088-2093. ScholarBank@NUS Repository.||Abstract:||This paper extends the well-known SMO 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.||Source Title:||Proceedings of the International Joint Conference on Neural Networks||URI:||http://scholarbank.nus.edu.sg/handle/10635/73868|
|Appears in Collections:||Staff Publications|
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