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ICIC
2005
Springer

Methods of Decreasing the Number of Support Vectors via k-Mean Clustering

9 years 28 days ago
Methods of Decreasing the Number of Support Vectors via k-Mean Clustering
This paper proposes two methods which take advantage of k -mean clustering algorithm to decrease the number of support vectors (SVs) for the training of support vector machine (SVM). The first method uses k -mean clustering to construct a dataset of much smaller size than the original one as the actual input dataset to train SVM. The second method aims at reducing the number of SVs by which the decision function of the SVM classifier is spanned through k -mean clustering. Finally, Experimental results show that this improved algorithm has better performance than the standard Sequential Minimal Optimization (SMO) algorithm.
Xiao-Lei Xia, Michael R. Lyu, Tat-Ming Lok, Guang-
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICIC
Authors Xiao-Lei Xia, Michael R. Lyu, Tat-Ming Lok, Guang-Bin Huang
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