Abstract: This paper proposes a kind of SVM classification，which is based on perceptron.This model in the classifier of training，has introduced the perceptron study thought，uses the support vector machines nuclear function to do nuclear calculation，then judges the classification of performance，classifying correctly does not make any revision，on the contrary，transforms to the perceptron study question.Experiments show that the model can not only improve the performance of SVM classification，but also can reduce the SVM classification performance to the nuclear function and parameters choice dependence.

Key words: Support Vector Machine（SVM）, Kernel function, perceptron, kernel parameter select

摘要： 提出了一种基于感知器的SVM分类模型（PSVM）。该模型在对分类器的训练中，引入感知器分类思想，其先利用SVM的核函数进行核计算，判断其分类性能，分类正确则不作任何修改，反之则转化成感知器分类问题。实验结果表明该模型不但能提高SVM的分类性能，而且还可以降低SVM分类性能对核函数及参数选择的依赖。

关键词: 支持向量机, 核函数, 感知器, 核参数选取