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SMO-based pruning methods for sparse least squares support vector machines
dc.contributor.author | Zeng, Xiangyan | |
dc.contributor.author | Chen, Xue-wen | |
dc.date.accessioned | 2007-05-03T23:45:48Z | |
dc.date.available | 2007-05-03T23:45:48Z | |
dc.date.issued | 2005-11 | |
dc.identifier.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 | |
dc.identifier.other | Digital Object Identifier 10.1109/TNN.2005.852239 | |
dc.identifier.uri | http://hdl.handle.net/1808/1512 | |
dc.description.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. | |
dc.language.iso | en_US | |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | |
dc.subject | Least squares support vector machine | |
dc.subject | Pruning | |
dc.subject | Sequential minimal optimization | |
dc.subject | Sparseness | |
dc.title | SMO-based pruning methods for sparse least squares support vector machines | |
dc.type | Article | |
dc.rights.accessrights | openAccess |