Presentation 2003/11/15
An Application of Fuzzy Support Vector Machines to Nonlinear Regression Problems
Masashi KURANOSHITA, Norikazu TAKAHASHI, Tetsuo NISHI,
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Abstract(in English) The problem to determine the functional relationship between the input variable and the output variable from given some pairs of the input value and the corresponding output value, which in general contains noise, is called the regression problem. In this report, we apply Fuzzy Support Vector Machines (FSVMs) to the regression problem. The FSVM is an extension of the SVM, which draws considerable attentions in the field of pattern recognition. Since a FSVM deals with each training sample according to the weight or the degree of importance of the sample, it can carry out more flexible pattern classifications than the original SVM. In this report, we first describe how a regression problem is solved by a SVM. Secondly we formulate the method of solving the problem by means of a FSVM and propose a way to determine the weight for each sample. Finally we show via computer simulations that FSVMs have a potential to improve the robustness.
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Keyword(in English) Nonlinear regression problem / Support vector machine / Fuzzy
Paper # NLP2003-125
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Conference Information
Committee NLP
Conference Date 2003/11/15(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An Application of Fuzzy Support Vector Machines to Nonlinear Regression Problems
Sub Title (in English)
Keyword(1) Nonlinear regression problem
Keyword(2) Support vector machine
Keyword(3) Fuzzy
1st Author's Name Masashi KURANOSHITA
1st Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University()
2nd Author's Name Norikazu TAKAHASHI
2nd Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University
3rd Author's Name Tetsuo NISHI
3rd Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University
Date 2003/11/15
Paper # NLP2003-125
Volume (vol) vol.103
Number (no) 464
Page pp.pp.-
#Pages 6
Date of Issue