关键词: 支持向量机, 预测精度, 参数优选, 加权误差, 蚁群算法

Abstract: Concerning the lack of an integrated theory system to select the parameters of combined kernel function used in Support Vector Machine (SVM), one method based on ant colony algorithm and circulated cross validation was put forward to get the optimal parameters. The index named as the mean weighting error was used to evaluate the effect of SVM prediction in different parameters. The value of mean weighting error could be calculated by circulated cross validation. To decrease the calculation workload, the ant colony algorithm was used to enhance the optimization effect of combined kernel function for SVM. This method offered in this paper was applied in the prediction of some plan development cost and the result showed that the optimized combined form of the parameters had the least prediction error. The instance indicates that the parameters optimization method in this paper can improve the prediction precision.

Key words: Support Vector Machine (SVM), prediction precision, parameters optimization, weighting error, ant colony algorithm