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Support vector machine (SVM) has been a promising method for classification and regression areas due to its solid statistical foundations, such as margin maximization and kernel methods. However, SVM is not typically used for large-scale data mining problems because its training complexity is highly dependent on the dataset size. This paper presents an improved granular support vector machine learning model based on hierarchical and dynamical granulation, namely, HD_GSVM, to solve the lowdoi:10.1016/j.neucom.2015.10.136 fatcat:ums4wlip6vdihk4swtddqu3uyy