SMO-based pruning methods for sparse least squares support vector machines.
Solutions of least squares support vector machines (LS-SVMs) are typically nonsparse. The sparseness is imposed by subsequently omitting data that introduce the smallest training errors and retraining the remaining data. Iterative retraining requires more intensive computations than training a single nonsparse LS-SVM. In this paper, we propose a new pruning algorithm for sparse LS-SVMs: the sequential minimal optimization (SMO) method is introduced into pruning process; in addition, instead of determining the pruning points by errors, we omit the data points that will introduce minimum changes to a dual objective function. This new criterion is computationally efficient. The effectiveness of the proposed method in terms of computational cost and classification accuracy is demonstrated by numerical experiments.
Zeng
Xiangyan
X
Department of Electrical and Computer Engineering, California State University, Northridge, CA 91003, USA.
Chen
Xue-Wen
XW
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016018:Least-Squares Analysis; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*