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Paper Abstract and Keywords
Presentation 2005-11-19 15:40
Application of minimum description length to Least Squares Support Vector Machines for modeling chaotic dynamical systems
Tsutomu Maeda, Masaharu Adachi (Tokyo Denki Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we attempt to prune the support vectors of Least Squares Support Vector Machines for function estimation by using a model selection algorithm based on Minimum Description Length principle.
However, it is difficult to improve the accuracy of the obtained model by pruning support vectors of LSSVM using the model selection algorithm.
As preliminary stage for the attempt, we apply the model selection algorithm to pruning support vectors of Support Vector Regression.
As examples of the modeling, we evaluate the proposing method in prediction of two chaotic dynamical systems.
As the result, we show that the number of support vectors is reduced by the algorithm.
Moreover, the accuracy of the obtained model improves in prediction accuracy for unknown data.
Keyword (in Japanese) (See Japanese page) 
(in English) Least Squares Support Vector Machines / Support Vector Regression / Minimum Description Length Principle / Pruning / / / /  
Reference Info. IEICE Tech. Rep., vol. 105, no. 417, NLP2005-83, pp. 71-76, Nov. 2005.
Paper # NLP2005-83 
Date of Issue 2005-11-12 (NLP) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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Conference Information
Committee NLP  
Conference Date 2005-11-18 - 2005-11-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyushu Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Special session and general. Subject of special session is ``Randomness and prediction---from fundamentals to applications''. 
Paper Information
Registration To NLP 
Conference Code 2005-11-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Application of minimum description length to Least Squares Support Vector Machines for modeling chaotic dynamical systems 
Sub Title (in English)  
Keyword(1) Least Squares Support Vector Machines  
Keyword(2) Support Vector Regression  
Keyword(3) Minimum Description Length Principle  
Keyword(4) Pruning  
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1st Author's Name Tsutomu Maeda  
1st Author's Affiliation Tokyo Denki University (Tokyo Denki Univ.)
2nd Author's Name Masaharu Adachi  
2nd Author's Affiliation Tokyo Denki University (Tokyo Denki Univ.)
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Speaker
Date Time 2005-11-19 15:40:00 
Presentation Time 25 
Registration for NLP 
Paper # IEICE-NLP2005-83 
Volume (vol) IEICE-105 
Number (no) no.417 
Page pp.71-76 
#Pages IEICE-6 
Date of Issue IEICE-NLP-2005-11-12 


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