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Kernel-based methods, e.g., support vector machine (SVM), produce high classification performances. However, the computation becomes time-consuming as the number of the vectors supporting the classifier increases. In this paper, we propose a method for reducing the computational cost of classification by kernel-based methods while retaining the high performance. By using linear algebra of a kernel Gram matrix of the support vectors (SVs) at low computational cost, the method efficiently prunesdoi:10.1109/icip.2009.5414339 dblp:conf/icip/KobayashiO09 fatcat:hanqydafzbgavaxw73mvwkhfqq