线性规划支持向量机

Support Vector Machine Based on Linear Programming

通过对统计学习理论中的支持向量回归问题,特别是结构风险问题和ε-不敏感函数的分析,得到了一种新的支持向量回归算法.新算法将传统的支持向量回归问题中的二次优化问题改进为线性规划问题,这一改进大大降低了求解的复杂度,其训练时间快了至少一个数量级以上.最后对人工和实际的样本进行了试验,结果说明了线性规划支持向量回归能较好地逼近被估计函数,且计算复杂度明显降低.

Based on analysis of the conclusions in the statistical learning theory, especially the structural risk minimization and the e-insensitive loss function, a novel linear programming support vector regression is proposed. The new algorithm reduces complexity and training time. Simulation results obtained using both artificial and real data show that the estimation performance of the proposed method is a good approximation of the unknown function and the computation complexity is significantly reduced,