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  Choosing nu in support vector regression with different noise models: theory and experiments

Chalimourda, A., Schölkopf, B., & Smola, A. (2000). Choosing nu in support vector regression with different noise models: theory and experiments. In IEEE-INNS-ENNS International Joint Conference on Neural Networks: IJCNN 2000: Neural Computing: New Challenges and Perspectives for the New Millennium (pp. 199-204). Piscataway, NJ, USA: IEEE.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E5C3-3 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-B5A6-3
Genre: Conference Paper

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=861457 (Publisher version)
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 Creators:
Chalimourda, A, Author
Schölkopf, B1, Author           
Smola, AJ, Author           
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: In support vector (SV) regression, a parameter /spl nu/ controls the number of support vectors and the number of points that come to lie outside of the so-called /spl epsi/-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of /spl nu/ that lead to the lowest generalization error. We find good agreement with the values that had previously been predicted by a theoretical argument based on the asymptotic efficiency of a simplified model of SV regression.

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 Dates: 2000-07
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 1837
DOI: 10.1109/IJCNN.2000.861457
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Title: IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000)
Place of Event: Como, Italy
Start-/End Date: 2000-07-27

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Title: IEEE-INNS-ENNS International Joint Conference on Neural Networks: IJCNN 2000: Neural Computing: New Challenges and Perspectives for the New Millennium
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 199 - 204 Identifier: ISBN: 0-7695-0619-4