摘要
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.
出处
《计算机科学》
CSCD
北大核心
2014年第4期230-232,243,共4页
Computer Science
基金
国家自然科学基金项目(61105059)资助
关键词
一类分类
支持向量机
回归算法
核函数
One-class classification
Support vector machine
Regression algorithm
Kernel function