Dynamic support vector machine by distributing kernel function

A dynamic support vector machine by distributing kernel function is put forward by integrating the target feature with the SVM. It distributes different Gauss kernel function to each training sample by using the distance between the target feature and each training sample. It is trained after the dynamic set is reconstructed according to the distance between the target feature and each training sample. Experiment results show that it is more robust than the traditional SVM.