Selection of the parameter in Gaussian kernels in support vector machine
Selection of the parameter in Gaussian kernels in support vector machine
复制标题
DOI：
10.1109/icccbda.2017.7951952
复制
发表时间：
2017-04
期刊：
影响因子：
--
通讯作者：
Yanyi Zhang;Rui Li
中科院分区：
文献类型：
--
作者：
Yanyi Zhang;Rui Li
支持向量机已成为分类领域的主要方法，也是监督学习的主要研究课题之一。该方法思想简单，实现速度快，在经济、自然科学和化工等领域得到了广泛的应用。高斯核函数是支持向量机方法中最常用的核函数，但其参数σ的选取至今尚未明确。本文研究了基于分离度和凝聚度的σ的选取问题。本文的数据是关于滁州职业技术学院专业评估的。本文的第二个目标是确定哪些专业表现良好，哪些专业不基于支持向量机方法。
Support vector machine has become a leading method in classifications and is one of the major topics in supervised learning. Its simple idea and fast implementation have made this method widely used in many areas, such as economics, natural science and chemical engineering. Gaussian kernel is the most common kernel in the support vector machine method, however, the selection of the parameter σ has not become clear yet. In this paper, we study the selection of σ based on separation and cohesion. The data is about the major evaluations in Chuzhou Vocational and Technical College. Our second goal in this paper is to determine which majors are performed well and which are not based on support vector machine method.