In order to solve classification problem of the intersections for multi-class classification based on hypersphere support vector machines

a hybrid decision strategy is put forward in this paper.First

characteristics of data distribution in the intersections are analyzed and then decision class is decided by different strategies.If training samples of two classes in the intersection can be classified by intersection hyper-plane for two hyper-spheres

then new test samples can be decided by this plane.If training samples of two classes in the intersection can be approximately linearly classified

new test samples can be classified by standard optimal binary-SVM hyper-plane.If training samples of two classes in the intersection can not be linearly classified

new test samples can be decided by introducing kernel function to get optimal classification hyper-plane.If training examples belong to only one class

then new test samples can be classified by exclusion method.Experimental results show performance of our algorithm is more optimal than hyper-sphere support vector machines with only one decision strategy.And this algorithm improves the performance and decreases computation complexity.