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  The entropy regularization information criterion

Smola, A., Shawe-Taylor J, Schölkopf, B., & Williamson, R. (2000). The entropy regularization information criterion. Advances in Neural Information Processing Systems, 342-348.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E4CA-0 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E4CB-E
Genre: Conference Paper

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 Creators:
Smola, AJ, Author
Shawe-Taylor J, Schölkopf, B1, Author           
Williamson, RC, Author
Solla, Editor
S.A., Editor
Leen, T.K., Editor
Müller, K-R, Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Effective methods of capacity control via uniform convergence bounds for function expansions have been largely limited to Support Vector machines, where good bounds are obtainable by the entropy number approach. We extend these methods to systems with expansions in terms of arbitrary (parametrized) basis functions and a wide range of regularization methods covering the whole range of general linear additive models. This is achieved by a data dependent analysis of the eigenvalues of the corresponding design matrix.

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 Dates: 2000-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-11245-0
URI: http://books.nips.cc/nips12.html
BibTex Citekey: 816
 Degree: -

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Title: Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999)
Place of Event: Denver, CO, USA
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Title: Advances in Neural Information Processing Systems
Source Genre: Journal
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 342 - 348 Identifier: -