支持向量机中的模型选择研究

Research on Model Selection of Support Vector Machine

支持向量机是一种新型的机器学习方法。模型选择是设计支持向量机的关键。本文在分析用于分类的支持向量机原理的基础上,分别从核函数类型和核参数的选择等模型选择方面进行了探讨。最后在上述理论分析的基础上进行了实验,取得了较好的效果。

Support vector machine （SVM） is a new method of machine learning. Model selection is essential to design SVM. Firstly, this paper introduces the theory of SVM for classification; Secondly, we discuss model selection from two aspects - the type of kernel function and parameters selection; Finally, experiment is performed and acquires a good result.