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SMO-based pruning methods for sparse least squares support vector machines

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2 Author(s)
Xiangyan Zeng ; Dept. of Electr. & Comput. Eng., California State Univ., Northridge, CA, USA ; Xue-wen Chen

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.

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Neural Networks, IEEE Transactions on  (Volume:16 ,  Issue: 6 )