Wavelet twin support vector machine

被引:0
作者
Shifei Ding
Fulin Wu
Zhongzhi Shi
机构
[1] China University of Mining and Technology,School of Computer Science and Technology
[2] Chinese Academy of Sciences,Key Laboratory of Intelligent Information Processing, Institute of Computing Technology
来源
Neural Computing and Applications | 2014年 / 25卷
关键词
SVM; TWSVM; Wavelet kernel function; WTWSVM;
D O I
暂无
中图分类号
学科分类号
摘要
Twin support vector machine (TWSVM) is a research hot spot in the field of machine learning in recent years. Although its performance is better than traditional support vector machine (SVM), the kernel selection problem still affects the performance of TWSVM directly. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and it is suitable for the analysis of local signals and the detection of transient signals. The wavelet kernel function based on wavelet analysis can approximate any nonlinear functions. Based on the wavelet kernel features and the kernel function selection problem, wavelet twin support vector machine (WTWSVM) is proposed by this paper. It introduces the wavelet kernel function into TWSVM to make the combination of wavelet analysis techniques and TWSVM come true. The experimental results indicate that WTWSVM is feasible, and it improves the classification accuracy and generalization ability of TWSVM significantly.
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收藏
页码:1241 / 1247
页数:6
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