关键词: 大规模支持向量机, 路径跟踪内点法, 矩阵LDL^T并行分解

Abstract: If the support vector machine is trained on large-scale datasets，the training time will be longer and generalization capability will be descended.Path following interior point method is proposed to design the SVM’s learning algorithm on large-scale datasets，and the key point being negative impact on SVM’s learning efficiency on large-scale datasets is to solve the large-scale iterative direction equations efficiently.To improve the SVM’s learning efficiency，the dimensions of direction equations are degraded，then LDLT parallel decomposition method is used to solve the direction sub-equations efficiently.The experimental results show that the new SVM’s training algorithm is efficient for large-scale datasets and the generalization capacity of SVM is not affected.

Key words: large-scale support vector machine, path following method, matrix LDL^T parallel decomposition