序列最小优化工作集选择算法的改进

序列最小化算法(SMO)是支持向量机重要的常用分解方法。而工作集的选择是实现序列最小优化算法的关键。通过重写KKT条件，提出了一种改进的新工作集选择方法，并相应提出最小化步骤。通过将改进的支持向量机方法应用于网络用户行为数据的分析，与现有方法进行对比测试，验证了新工作集选择方法将减少支持向量机的学习时间并加快收敛过程，改进的支持向量机方法在运行效率和准确度上都有不同程度的提高。

Improvement of Working Set Selection for SMO Method

Support Vector Machine (SVM) as a machine learning method has rigorous theoretical basis and been used widely in engineering practices. How to improve the convergence speed of SVM is the hot research topic. Sequential Minimal Optimization (SMO) algorithm is an important decomposition method for SVM, in particularly, the working set selection algorithm is the key part for SMO. In most cases, the working set selection is selected directly based on the KKT conditions, but in some situations, the conventional selection method can't avoid the shortcoming resulting from random selection, even can't satisfy with the KKT conditions. In this paper, the condition of working set selection is improved by rewriting the KKT conditions, and a set of new steps for minimizing SMO sub-problem is proposed accordingly. The improved method is used to analyze the users' online behaviors, by comparing with the existing methods. It is validated that the new working set selection algorithms can reduce the learning time and accelerate the convergence speed of support vector machine, and improves the computational accuracy.