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  Statistical Learning Theory, Capacity and Complexity

Schölkopf, B. (2003). Statistical Learning Theory, Capacity and Complexity. Complexity, 8(4), 87-94. doi:10.1002/cplx.10094.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DC18-E Version Permalink: https://hdl.handle.net/21.11116/0000-0005-6A4F-D
Genre: Journal Article

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https://onlinelibrary.wiley.com/doi/epdf/10.1002/cplx.10094 (Publisher version)
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 Creators:
Schölkopf, B1, 2, 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, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We give an exposition of the ideas of statistical learning theory, followed by a discussion of how a reinterpretation of the insights of learning theory could potentially also benefit our understanding of a certain notion of complexity.

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 Dates: 2003-07
 Publication Status: Issued
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 Rev. Type: -
 Identifiers: DOI: 10.1002/cplx.10094
BibTex Citekey: 2364
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Title: Complexity
Source Genre: Journal
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Publ. Info: New York, N.Y. : Wiley
Pages: - Volume / Issue: 8 (4) Sequence Number: - Start / End Page: 87 - 94 Identifier: ISSN: 1076-2787
CoNE: https://pure.mpg.de/cone/journals/resource/954933184423