Title :
SMO algorithm for least squares SVM
Author :
Keerthi, S.S. ; Shevade, S.K.
Author_Institution :
Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore
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.
Keywords :
convergence of numerical methods; iterative methods; least squares approximations; optimisation; support vector machines; SMO algorithm; convergence; iterative methods; least squares SVM; sequential minimal optimisation; support vector machines; Character generation; Computer science; Equations; Kernel; Large-scale systems; Least squares methods; Linear matrix inequalities; Mechanical engineering; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223730
