一种支持向量机参数选择的改进分布估计算法

支持向量机(support vector machine,SVM)的学习性能和泛化能力在很大程度上取决于参数的合理设置. 将支持向量机的参数选择问题转化为优化问题,以模型预测均方根误差为评价函数,提出一种引入混沌变异操作的改进分布估计算法(estimation of distributionalgorithm,EDA),并将其用于优化求解ε-支持向量机的参数:惩罚因子、不敏感损失系数以及高斯径向基核函数的宽度. 由于改进EDA利用混沌运动的随机性和遍历性等特点在解空间内进行优化搜索,能够较好解决传统EDA易于陷入局部极小的缺陷. Chebyshev混沌时间序列预测仿真结果表明:改进EDA是选取SVM参数的有效方法.

Parameters selection of a support vector machine using an improved estimation of the distribution algorithm

The learning performance and the generalization property of support vector machines (SVMs) are greatly  influenced by the suitable setting of some parameters. The parameters selection can be transformed into an optimization problem by defining the root mean square error of a SVM prediction model as an evaluation function. A kind of improved estimation of the distribution algorithm (EDA) with a chaotic-mutation operation was proposed and used to optimize parameters of a ε-SVM including a penalty factor, an insensitive loss coefficient and a width of a Gaussian kernel function. The improved EDA could take advantage of the randomness and ergodicity of chaos, which  could  solve the local minima problem of traditional EDAs. Simulation result of the prediction of a Chebyshev chaotic time series showed that the improved EDA was an effective method of solving the problem for parameters selection of a SVM.