Comparison of the accuracy of SVM kernel functions in text classification

被引：0

作者：

Kalcheva, Neli ^{[1
]}

Karova, Milena ^{[2
]}

Penev, Ivaylo ^{[2
]}

机构：

[1] Tech Univ Varna, Dept Software & Internet Technol, Varna, Bulgaria

[2] Tech Univ Varna, Dept Comp Sci & Engn, Varna, Bulgaria

来源：

PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON BIOMEDICAL INNOVATIONS AND APPLICATIONS (BIA 2020) | 2020年

关键词：

Support Vector Machines; SVM; Support Vector Classification; SVC; text classification; machine learning; kernel functions; kernels poly; rbf; sigmoid; linear;

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：

The objective of this paper is to compare the accuracy of different kernel functions of the SVM method for text classification. As a basis for the research film reviews are used. The authors try to detect the kernel functions and their parameters to achieve high accuracy in movie reviews classification. The studied kernel functions are: polynomial kernel of degree 2, a linear kernel and a radial base kernel. The achieved accuracy is higher than 83%. The experiments show that the sigmoid radial kernel is an inappropriate choice in text classification.

引用

页码：141 / +

页数：5

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