The kernel parameter and penalty parameter C are the main factors that affect the learning performance of the support vector machine. However, there are many deficiencies in the existing kernel parameters and penalty parameters C. These methods do not have high accuracy when it comes to classifying multi-category samples, and even ignore some of the samples to conduct training, which violates the integrity of the experimental data. In contrast, this paper improves the selection method of support vector machine kernel parameters and penalty parameters in two ways. First, it obtains the kernel parameter value by optimizing the maximum separation interval between the samples. Second, it optimizes the generalization ability estimation via the influence of the non-boundary support vector on the stability of the support vector machine. The method takes full account of all the training sample data, which is applicable to most sample types, and has the characteristics of low initialization requirements and high-test accuracy. The paper finally uses multiple sets of UCI sample data sets and facial image recognition to verify the method. The experimental results show that the method is feasible, effective and stable.

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uci sample data sets learning performance generalization ability classifying multicategory samples separation interval methods penalty parameters facial image recognition accuracy selection method initialization support vector machine kernel parameters stability of

Mingming Yang,Ling Sun,Yangyang Chen,Jian Bao,.Research on parameter selection method for support vector machines. 48 (2),331–342.

Mingming Yang, et al."Research on parameter selection method for support vector machines" 48

Mingming Yang,Ling Sun,Yangyang Chen,Jian Bao,"Research on parameter selection method for support vector machines"