支持向量机回归的参数选择方法

Parameters Selection Method for Support Vector Machine Regression

综合4种支持向量机回归的参数选择方法的优点,提出一种对训练样本进行分析并直接确定参数的方法。在标准测试数据集上的试验证明,该方法与传统网格搜索法相比,在时间和预测精度方面取得了更好的结果,可以较好地解决支持向量机在实际应用中参数难以选择、消耗时间长的问题。

By combining several parameters selection approaches of Support Vector Machine（SVM）, this paper proposes a method that defines parameters directly by analyzing training samples. Experimental results based on several standard test data sets show that the method achieves better prediction accuracy and consumes less time compared to traditional grid search methods. It provides one way to deal with the problem of selecting parameters and time consuming in application of SVM.