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  Support Vector Regression for Black-Box System Identification

Gretton, A., Doucet, A., Herbrich, R., Rayner, P., & Schölkopf, B. (2001). Support Vector Regression for Black-Box System Identification. In 11th IEEE Workshop on Statistical Signal Processing (pp. 341-344). Piscataway, NJ, USA: IEEE.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E37C-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-A87B-4
Genre: Conference Paper

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 Creators:
Gretton, A1, Author              
Doucet, A, Author
Herbrich, R, Author
Rayner, P, Author
Schölkopf, B1, Author              
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box system identification. These methods derive from statistical learning theory, and are of great theoretical and practical interest. We briefly describe the theory underpinning SVR, and compare support vector methods with other approaches using radial basis networks. Finally, we apply SVR to modeling the behaviour of a hydraulic robot arm, and show that SVR improves on previously published results.

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 Dates: 2001-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 1851
DOI: 10.1109/SSP.2001.955292
 Degree: -

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Title: 11th IEEE Workshop on Statistical Signal Processing (SSP 2001)
Place of Event: Singapore
Start-/End Date: 2001-08-08

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Title: 11th IEEE Workshop on Statistical Signal Processing
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 341 - 344 Identifier: ISBN: 0-7803-7011-2