核函数对v-支持向量机的泛化能力影响分析

支持向量机在模式分类中得到了很好的应用。对于线性不可分的样本空间，需要寻找核函数，将线性不可分的样本集映射到另一个高维线性空间。支持向量机的泛化能力直接影响其实际应用效果。本文讨论了v-支持向量机的泛化能力的一个估计，定性分析了核函数对v-支持向量机的泛化能力的影响，并就高斯核函数对v-支持向量机的泛化能力的
的影响进行了具体分析。

Analysis of the Influence of the Kernel Function on the Generalization Capacity of v-SVM

Support vector machine has been effectively applied in pattern classification. For a sample space which is linearly inseparable,we need to find the kernel function in order to map the sample set, which is linearly inseparable, to another high-dimension linear space. The effectiveness of the application of v-SVM is directly influenced by its generalization capability. In this article,we discuss an estimation of the generalization capability of v-SVM, qualitatively analyze the influence on the generalization capability of v-SVM by the kernel function, and quantitatively analyze such influence by the Gaussian kernel function.