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  New Support Vector Algorithms

Schölkopf, B., Smola, A., Williamson, R., & Bartlett, P. (2000). New Support Vector Algorithms. Neural computation, 12(5), 1207-1245. doi:10.1162/089976600300015565.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E4E5-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-B070-5
Genre: Journal Article

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Schölkopf, B1, Author              
Smola, AJ1, Author              
Williamson, RC, Author
Bartlett, PL, Author
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1External Organizations, ou_persistent22              

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 Abstract: We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter nu} lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter {epsilon} in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of {nu, and report experimental results.

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 Dates: 2000-05
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1162/089976600300015565
BibTex Citekey: 734
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Title: Neural computation
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: 12 (5) Sequence Number: - Start / End Page: 1207 - 1245 Identifier: ISSN: 0899-7667
CoNE: https://pure.mpg.de/cone/journals/resource/954925561591