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SMO-based pruning methods for sparse least squares support vector machines
Zeng, Xiangyan Chen, Xue-wen
Zeng, Xiangyan
Chen, Xue-wen
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Abstract
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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Date
2005-11
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Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Keywords
Least squares support vector machine, Pruning, Sequential minimal optimization, Sparseness
Citation
Zeng, XY; Chen, XW. SMO-based pruning methods for sparse least squares support vector machines. IEEE TRANSACTIONS ON NEURAL NETWORKS. November 2005. 16(6) : 1541-1546