摘要
针对支持向量机中混合核函数参数的选取还没有一套完整的理论支撑,提出基于蚁群算法和循环交叉验证法的参数优选方法。以平均加权误差作为不同参数下支持向量机预测效果的评价指标,采用循环交叉验证法计算平均加权误差。采用蚁群算法来提高混合核函数参数优化效率,减少计算工作量。通过在某型飞机机体研制费用预测中的应用,显示基于最优参数下混合核函数的支持向量机的预测误差最小,表明该参数优选方法能够提高预测精度。
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.
出处
《计算机应用》
CSCD
北大核心
2013年第5期1321-1323,1356,共4页
journal of Computer Applications
关键词
支持向量机
预测精度
参数优选
加权误差
蚁群算法
Support Vector Machine （SVM）
prediction precision
parameters optimization
weighting error
ant colony algorithm
