关键词: 支持向量机, 路径跟踪算法, 核距离矩阵, 泛化能力

Abstract: If the Support Vector Machine（SVM） is trained on large-scale datasets，the training time will be longer and the generalization capability will be descended.The time complexity of the path following interior point method is [O(nL)]，so it has been used to solve many large-scale Quadratic Programming（QP） problems.The main factors for constructing the separating hyper-plane of SVM are stated.The path following method and kernel distance matrix are used to reduce the training datasets，and the SVM is retrained with the reduced datasets.The experimental results show that the SVM model is simpler and the generalization capability is enhanced after using the reduced datasets to train the SVM.

Key words: Support Vector Machine（SVM）, path following method, kernel distance matrix, generalization capacity