Yuanhai Shao

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Support vector machines (SVM) have been promising methods for classification analysis due to their solid mathematical foundations. Clustering-based SVMs are used to solve large samples classification problems and reduce the computational cost. In this paper, we present a density clustering based SVM(DCB-SVM) method to predict polyadenylation signal (PAS) in(More)
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