Fuzzy support vector regression for function approximation with noises

Author

Zhang, Rui ; Duan, Xian-Bao ; Hao, Lei

Author_Institution

Sch. of Sci., Shandong Univ. of Technol., Zibo, China

Volume

11

fYear

2010

fDate

22-24 Oct. 2010

Abstract

Fuzzy support vector machine (FSVM) have been very successful in pattern recognition problems with outliers or noises. FSVM enhances the SVM in reducing the effect of noises in data points. In this paper, we introduce FSVM to regression problems for function approximation with noises. We apply a fuzzy membership to each input point of SVR and reformulate SVR into fuzzy SVR (FSVR) such that different input points can make different contributions to the learning of decision function.

Keywords

decision making; function approximation; fuzzy set theory; learning (artificial intelligence); pattern classification; regression analysis; signal denoising; support vector machines; data point; decision function; function approximation; fuzzy membership; fuzzy support vector machine; noise effect reduction; pattern recognition; regression problem; Artificial neural networks; Function approximation; Noise; Support vector machines; Testing; Training; Training data; FSVM; SVM; SVR; regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Application and System Modeling (ICCASM), 2010 International Conference on

Conference_Location

Taiyuan

Print_ISBN

978-1-4244-7235-2

Electronic_ISBN

978-1-4244-7237-6

Type

conf

DOI

10.1109/ICCASM.2010.5623271

Filename

5623271