Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (14): 52-55.
• 学术探讨 •
王强 陈英武 邢立宁
In order to improve support vector regression (SVR) learning ability and generalization performance, a joint optimization method for selecting features and SVR parameters is proposed. By using the method, a new set of uncorrelated features is obtained by using principal component analysis , the mean squared error(MSE) is taken into account for the accuracy evaluation of SVR, and a real-coding based immune genetic algorithm is employed to solve the joint optimization problem. Simulation experiments show that the joint optimization method guarantees better regression accuracy and the optimization process has a higher rate than the single optimization of features or SVR parameters.
王强 陈英武 邢立宁. 特征选择和支持向量回归参数的联合优化[J]. 计算机工程与应用, 2007, 43(14): 52-55.
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