Abstract: Considering the deficiencies of traditional ways，one sort of combinatorial simulated annealing algorithms is introduced to establish a new method for the SVM parameters selection.In this method，appropriate objective function is set to guarantee SVM’ maximum generalization ability.Meanwhile，by referring to the cross-validation principle，are the samples in the training set utilized to select SVM models，and the samples in the testing set to search optimal parameters.Finally，a comparative analysis is conducted upon the data of simulation experiments between the proposed approach and those based on genetic algorithms and refined mesh algorithms.The results show that this method is endowed with a better global search performance and a higher convergence rate，which make it an effective way to determine optimal SVM parameters，and therefore enjoys a strong practical value.

摘要： 针对传统方法的不足，提出将一种模拟退火组合算法用于支持向量机的参数选择，将优化指标设定为最大化SVM的泛化能力，并据此确立适当的目标函数；同时借鉴交叉检验的思想，建立以训练集和测试集中的数据分别选择模型和搜索最优参数组合的研究手段。最后，在仿真实验的基础上同基于遗传算法和精化网格法的选取方法进行了对比分析，结果表明该组合算法具有更好的全局搜索性能和收敛速度，是SVM参数选取的一种有效方法，具有较强的实用价值。