In practical application of text classification for specific domains, most of the text always dopes with each other and is unable to be expressed in linear. The application of kernel function in SVM can solve the problem of nonlinear classification effectively. Different SVM can be constructed by different kernel function, and the recognition performance is also different. So the key problems of SVM are choosing the appropriate kernel function and optimizing its parameters. This paper constructs a new combination kernel function combined with homogeneous polynomial kernel and radial basis kernel function by linear weighted method based on the character of the kernel function. The combination kernel function has good generalization ability and good earning ability at the same time. The simulation experiment result shows that the precision rate, recall rate and comprehensive classification rate of macro average of combination kernel function are obviously better than linear kernel, polynomial kernel and radial basis kernel in choosing the right parameters, and the precision rate and the recall rate are ideal.%面向特定领域文本分类的实际应用,存在大量样本相互掺杂的现象,使其无法线性表述,在SVM中引入核函数可以有效地解决非线性分类的问题,而选择不同的核函数可以构造不同的 SVM,其识别性能也不同,因此,选择合适的核函数及其参数优化成为 SVM 的关键。本文基于单核核函数的性质,对多项式核函数与径向基核函数进行线性加权,构建具有良好的泛化能力与良好的学习能力的组合核函数。仿真实验结果表明,在选择正确参数的情况下,组合核函数SVM的宏平均准确率、宏平均召回率及宏平均综合分类率都明显优于线性核、多项式核与径向基核,而且能够兼顾准确率与召回率。
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