An algorithm to cluster data for efficient classification of support vector machines - Li, Der-Chiang; Fang, Yao-Hwei (Expert systems with applications, 2008)
Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters, and finds critical data in clusters as a substitute for the original data to reduce the computational complexity. The computational experiments presented in this paper show that the clustering algorithm significantly advances SVM learning efficiency.