Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/73868
Title: SMO Algorithm for Least Squares SVM
Authors: Keerthi, S.S. 
Shevade, S.K.
Issue Date: 2003
Source: 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
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