计算机与数字工程2017，Vol.45Issue(12)：2479-2481,2497,4.DOI:10.3969/j.issn.1672-9722.2017.12.032
基于支持向量机和稀疏表示的文本分类研究
Text Classification Using Combined Sparse Representation Classifiers and Support Vector Machines
刘国锋1
作者信息
- 1. 江苏科技大学计算机科学与工程学院 镇江 212003
- 折叠
摘要
Abstract
Text classification is very important for various fields of management of a large number of text content,based on text classification based on the frequency of kernel function,the advantages and disadvantages of all kinds of classifiers are com?pared,this paper proposes a sparse classifier(SRC)and support vector machine(SVM)combination method to improve the perfor?mance of text classification.Sparse representation classifier the field dictionary is constructed by means of the vector representation of the document. SVM text classifier linear kernel function and Gauss kernel function are used for the vector representation of the document.关键词
稀疏表示/SVM/频率核函数/文本分类Key words
sparse representation/SVM/frequency-based kernels/text classification分类
信息技术与安全科学引用本文复制引用
刘国锋..基于支持向量机和稀疏表示的文本分类研究[J].计算机与数字工程,2017,45(12):2479-2481,2497,4.
