Abstract:

With the development of the support vector machine (SVM), the kernel function has become one of the cores of the research on SVM. To a large extent, the kernel function determines the generalization ability of the classifier, but there is still no general theory to guide the choice and structure of the kernel function. An ensemble kernel function model based on the game theory is proposed, which is used for the SVM classification algorithm. The model can effectively integrate the advantages of the local kernel and the global kernel to get a better classification result, and can provide a feasible way for structuring the kernel function. By making experiments on some standard datasets, it is verified that the new method can significantly improve the accuracy of classification.