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.

• Type: Conference
• Year: 2005
• Where: ICIC
• Authors: Xiao-Lei Xia, Michael R. Lyu, Tat-Ming Lok, Guang-Bin Huang
• Keywords: -mean Clustering, -mean Clustering Algorithm, ICIC 2005, Support Vector