基于一类分类的线性规划支持向量回归算法

Linear Programming Support Vector Regression Method Based on One-class Classification

根据一类分类思想,提出一种基于线性规划的支持向量回归算法,该算法揭示了一类分类和回归之间的关系。实验在一个正弦函数、一个混沌时间序列和一个实际的数据上进行。实验结果表明,所给算法的泛化性能优于标准的支持向量回归算法(ε-SVR)、线性规划支持向量回归算法(LP-SVR)和最小二乘支持向量回归算法(LS-SVR),实验结果也说明了所给算法的有效性和可行性。

A new support vector regression algorithm based on linear programming was proposed according to one-class classification,which can more uncover the relation between one-class classification and regression.The tests were perform on sine function,chaos time series and real world data sets.Experiments show that the new method has comparable or better generalization performance than e-insensitive Support Vector Regression （e-SVR）,Linear Programming Support Vector Regression （LP-SVR） and Least squares support vector regression （LS-SVR）,and also show that the proposed method is feasible and valid.