A continuous wavelet kernel for support vector regression

被引：0
作者：
Han, Fengqing [1 ]
Gao, Yanghua
Ma, Li
Li, Hongmei
Liao, Xiaofeng
机构：
[1] Chongqing Inst Meteorolog Sci, Postdoctoral Programme, Chongqing 401147, Peoples R China
[2] Chongqing Inst Chongqing Technol, Sch Comp Sci & Engn, Chongqing 400050, Peoples R China
[3] Chongqing Univ, ISCI Lab, Chongqing 400044, Peoples R China
来源：
DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS | 2006年 / 13E卷
关键词：
D O I：
暂无
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A new kernel function is proposed for support vector regression(SVR). An one-to-one mapping is adopted for dimensionality reduction and then continuous wavelet transform is utilized to construct the nonlinear mapping phi(x) from the input space S to the feature space. So we call it continuous wavelet kernel function(CWKF). This wavelet kernel is not translation invariant kernel, instead inner product kernel and need not parameter selecting. The quadratic program of support vector regression has feasible solution if we use CWKF. Numerical experiments demonstrate the effectiveness of this method.
引用
收藏
页码：3617 / 3620
页数：4
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