Fuzzy Clustering Multiple Kernel Support Vector Machine

Abstract

In order to improve the classification speed and accuracy of support vector machines, a fuzzy clustering multi-kernel support vector machine algorithm is proposed. In this paper, the fuzzy clustering method is used to cluster the training datasets into several clusters. By introducing effective clustering centers, the training of the original training datasets is simplified to the training of the effective clustering center datasets. So as to reduce the training time and improve the training accuracy. At the same time, this paper uses Multiple Kernel Support Vector Machine to replace the traditional single kernel support vector machine to carry on the operation, which can handle complex data structures and improve the training precision effectively. Numerical experiments show that the fuzzy clustering Multiple Kernel Support Vector Machine has the advantages of higher classification accuracy and shorter classification time than the traditional Multiple Kernel support vector machine.