Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications
Online ISSN : 2188-4749
Print ISSN : 2188-4730
The 33rd ISCIE International Symposium on Stochastic Systems Theory and Its Applications (Oct. 2001, Tochigi)
Support Vector Machine and Its Application to System Identificatio
Shuichi Adachi
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2002 Volume 2002 Pages 170-175

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Abstract
Support Vector Machines (SVMs) have become a subject of intensive study in statistical learning theory. They have been applied to successfully to classification problems and recently extended to regression problems. Support vector machines for regression problems is called Support Vector Regression (SVR). In this paper, a brief introduction to SVR is presented and then a new system identification method based on SVR is proposed for linear in parameter models. The effectiveness of the proposed method is examined through numerical examples.
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© 2002 ISCIE Symposium on Stochastic Systems Theory and Its Applications
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