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.
引用
收藏
页码：1241 / 1247
页数：6
相关论文
共 48 条
[1]  
Ding S(2011)An overview on theory and algorithm of support vector machines J Univ Electron Sci Technol China 40 2-10
[2]  
Qi B(1995)Support-vector networks Mach Learn 20 273-297
[3]  
Tan H(2013)Support vector machines used to estimate the battery state of charge IEEE Trans Power Electron 28 5919-5926
[4]  
Cortes C(2013)Determination of quality parameters in moist wood chips by near infrared spectroscopy combining PLS-DA and support vector machines J Wood Chem Technol 33 247-257
[5]  
Vapnik V(2013)A feature-selection algorithm based on support vector machine-multiclass for hyperspectral visible spectral analysis J Food Eng 119 159-166
[6]  
Anton JCA(2013)A new and informative active learning approach for support vector machine Inf Sci 244 142-160
[7]  
Nieto PJG(2013)Using polynomial kernel support vector machines for speaker verification IEEE Signal Process Lett 20 901-904
[8]  
Viejo CB(2013)Seismic reliability assessment of RC structures including soil-structure interaction using wavelet weighted least squares support vector machine Reliab Eng Syst Saf 110 22-33
[9]  
Vilan JAV(2006)Multisurface proximal support vector machine classification via generalized eigenvalues IEEE Trans Pattern Anal Mach Intell 28 69-74
[10]  
Nascimbem LBLR(2007)Twin support vector machines for pattern classification IEEE Trans Pattern Anal Mach Intell 29 905-910
12345