Xuechen Liu

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The convex quadratic programming problem, involved in the large scale support vector machine (SVM) training phase, is computationally expensive. Interior Point Methods (IPM) have been used successfully to solve this problem. They have polynomial time complexity and maintain a constant predictable structure of the linear system that needs to solve each(More)
Support vector machine (SVM) is a supervised method widely used in the statistical classification and regression analysis. SVM training can be solved via the interior point method (IPM) with the advantages of low storage, fast convergence and easy parallelization. However, it is still confronted with the challenges of training speed and memory use. In this(More)
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