Title :
The Beat-wave signal regression based on least squares reproducing kernel support vector machine
Author :
Deng, Cai-xia ; Xu, Li-xiang ; Fu, Zuo-xian
Author_Institution :
Appl. Sci. Coll., Harbin Univ. of Sci. & Technol., Harbin
Abstract :
The kernel function of support vector machine(SVM) is an important factor for studying the result of the SVM. Based on the conditions of the support vector kernel function and reproducing kernel(RK) theory, a novel notion of least squares RK support vector machine(LS-RKSVM) with a RK on the Sobolev Hilbert space H1(R;a,b) is proposed for regressing Beat-wave signal. The choice of the RK is important in SVM technic. The RK function enhances the generalization ability of least squares support vector machine(LS-SVM) method. The simulation results are presented to illustrate the feasibility of the proposed method, this model gives a better experiment results.
Keywords :
Hilbert spaces; least mean squares methods; regression analysis; signal processing; support vector machines; Sobolev Hilbert space; beat-wave signal regression; kernel support vector machine; least squares support vector machine; reproducing kernel; support vector kernel function; Face recognition; Function approximation; Handwriting recognition; Image recognition; Kernel; Least squares methods; Machine learning; Speech recognition; Support vector machines; Text recognition; Kernel function; Reproducing kernel; SVM; Signal regression;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4621037