Selection of the Parameter in Gaussian Kernels in Support Vector Machine

被引：0

作者：

Zhang, Yanyi ^{[1
]}

Li, Rui ^{[1
]}

机构：

[1] Chuzhou Vocat & Tech Coll, Chuzhou, Peoples R China

来源：

2017 2ND IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA 2017) | 2017年

关键词：

support vector machine; Gaussian kernels; separation; cohesion; major evaluations; PREDICTION; MODEL;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 sigma has not become clear yet. In this paper, we study the selection of sigma 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.

引用

页码：430 / 433

页数：4

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