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  Kernel Methods for Implicit Surface Modeling

Schölkopf, B., Giesen, J., & Spalinger, S. (2005). Kernel Methods for Implicit Surface Modeling. Advances in Neural Information Processing Systems, 1193-1200.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D521-0 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D522-E
Genre: Conference Paper

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 Creators:
Schölkopf, B1, Author           
Giesen, J, Author
Spalinger, S, Author
Saul, Editor
L.K., Editor
Weiss, Y., Editor
Bottou, L., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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Free keywords: -
 Abstract: We describe methods for computing an implicit model of a hypersurface that is given only by a finite sampling. The methods work by mapping the sample points into a reproducing kernel Hilbert space and then determining regions in terms of hyperplanes.

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 Dates: 2005-07
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-19534-8
URI: http://books.nips.cc/nips17.html
BibTex Citekey: 2814
 Degree: -

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Title: Eighteenth Annual Conference on Neural Information Processing Systems (NIPS 2004)
Place of Event: Vancouver, BC, Canada
Start-/End Date: -

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Title: Advances in Neural Information Processing Systems
Source Genre: Journal
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Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1193 - 1200 Identifier: -