Large-scale support vector machine based on kernel space and samples' center angle

To improve the training accuracy of support vector machine when processing large-scale data sets,in kernel-induced feature spaces,a support vector machine algorithm based on kernel-induced feature spaces is proposed.Firstly the two centers of the original training sets and vertical hyperplane of the two centers are gotten,then the ratio of the distance from the sample in original training sets is obtained to the vertical hyperplane and distance from it to the midpoint of the two centers,finally n samples are used with the smallest ratios to train instead of original training sets.The last mathematical model shows that the algorithm does not require calculation of the kernel-induced feature spaces,can retain more incremental support vectors to ensure the training accuracy than the other decrement strategies.With examples,a simulation analysis of the algorithm is given out.The results show that,compared with similar algorithms,it gets more training accuracy without training speed reduced basically.