The support vector machine (SVM) is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal parameter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search e?ciency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation. The support vector machine (SVM) is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is considered as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal parameter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search e? ciency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.