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  Experimentally optimal ν in support vector regression for different noise models and parameter settings

Chalimourda, A., Schölkopf, B., & Smola, A. (2005). Experimentally optimal ν in support vector regression for different noise models and parameter settings. Neural networks, 18(2), 205-205. doi:10.1016/j.neunet.2004.11.001.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D5E3-D Version Permalink: http://hdl.handle.net/21.11116/0000-0004-DC97-A
Genre: Journal Article

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Chalimourda, A, Author
Schölkopf, B1, 2, Author              
Smola, AJ, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Dates: 2005-03
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1016/j.neunet.2004.11.001
BibTex Citekey: 4679
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Title: Neural networks
Source Genre: Journal
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Publ. Info: New York : Pergamon
Pages: - Volume / Issue: 18 (2) Sequence Number: - Start / End Page: 205 - 205 Identifier: ISSN: 0893-6080
CoNE: https://pure.mpg.de/cone/journals/resource/954925558496