DocumentCode :
3351428
Title :
Function approximation based on Twin Support Vector Machines
Author :
Yang, Chengfu ; Yi, Zhang ; Zuo, Lin
Author_Institution :
Sch. of Comput. Sci. & Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu
fYear :
2008
fDate :
21-24 Sept. 2008
Firstpage :
259
Lastpage :
264
Abstract :
A new function approximation algorithm based on twin support vector machines (TSVM) is presented in this paper. Support vector regression (SVR) has been shown to have good robust properties against noise in function approximation, however, the overfitting phenomena cannot be eliminated if the parameters used in SVR are improperly selected, and the selection of various parameters is not straightforward. In this paper, we use the properties of TSVM to solve this problem, that it will generate two nonparallel planes such that each plane is closer to one of the two classes and is as far as possible from the other. The experiments show good performances without additional computing time by using traditional test functions, data with noise and ambiguous training data.
Keywords :
function approximation; regression analysis; support vector machines; function approximation; support vector regression; twin support vector machines; Approximation algorithms; Artificial neural networks; Computational intelligence; Computer science; Function approximation; Interpolation; Laboratories; Noise robustness; Support vector machines; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetics and Intelligent Systems, 2008 IEEE Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-1673-8
Electronic_ISBN :
978-1-4244-1674-5
Type :
conf
DOI :
10.1109/ICCIS.2008.4670876
Filename :
4670876
Link To Document :
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