SMO algorithm for least squares SVM
Keerthi, S.S.
Shevade, S.K.
Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore;
This paper appears in: Neural Networks, 2003. Proceedings of the International Joint Conference on
Publication Date: 20-24 July 2003
Volume: 3,
On page(s): 2088- 2093 vol.3
ISSN: 1098-7576
ISBN: 0-7803-7898-9
INSPEC Accession Number: 7892017
Digital Object Identifier: 10.1109/IJCNN.2003.1223730
Current Version Published: 2003-08-26
Abstract
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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