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  Kernel-dependent support vector error bounds

Schölkopf, B., Shawe-Taylor, J., Smola, A., & Williamson, R. (1999). Kernel-dependent support vector error bounds. In Ninth International Conference on Artificial Neural Networks ICANN 99 (pp. 103-108). London, UK: Institute of Electrical Engineers.

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Schölkopf, B1, Author              
Shawe-Taylor, J, Author
Smola, AJ, Author              
Williamson, RC, Author
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1External Organizations, ou_persistent22              

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 Abstract: Model selection in support vector machines is usually carried out by minimizing the quotient of the radius of the smallest enclosing sphere of the data and the observed margin on the training set. We provide a new criterion taking the distribution within that sphere into account by considering the eigenvalue distribution of the Gram matrix of the data. Experimental results on real world data show that this new criterion provides a good prediction of the shape of the curve relating generalization error to kernel width.

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 Dates: 1999-09
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 810
DOI: 10.1049/cp:19991092
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Title: Ninth International Conference on Artificial Neural Networks (ICANN 1999)
Place of Event: Edinburgh, UK
Start-/End Date: 1999-09-07 - 1999-09-10

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Title: Ninth International Conference on Artificial Neural Networks ICANN 99
Source Genre: Proceedings
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Publ. Info: London, UK : Institute of Electrical Engineers
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 103 - 108 Identifier: ISBN: 0-85296-721-7

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Title: Conference Publication of the Institution of Electrical Engineers
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Pages: - Volume / Issue: 470 Sequence Number: - Start / End Page: - Identifier: -