A Method of Optimizing Kernel Parameter of Sphere Structured Support Vector Machine

Sphere structured support vector machine is a multi-classification algorithm. The algorithm separately constructs sphere for each class sample data, so the complex degree of the quadratic programming is reduced and it is easier to extend new samples. But, kernel parameter selection of sphere structured support vector machine needs to predetermine the parameter search interval. For eliminating human experience factors, a kernel parameter optimization method based on sphere center distance is proposed. Experimental results show that the proposed method can greatly shorten the training time in the case of the average classification accuracy of the classifier is not reduced.