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  Branch and Bound for Semi-Supervised Support Vector Machines

Chapelle, O., Sindhwani, V., & Keerthi, S. (2007). Branch and Bound for Semi-Supervised Support Vector Machines. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 217-224.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CBD7-7 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CBD8-5
Genre: Conference Paper

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 Creators:
Chapelle, O1, Author           
Sindhwani, V, Author
Keerthi, SS, Author
Schölkopf, Editor
B., Editor
Platt, J., Editor
Hofmann, T., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Semi-supervised SVMs (S3VMs) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The associated optimization problem is non-convex. To examine the full potential of S3VMs modulo local minima problems in current implementations, we apply branch and bound techniques for obtaining exact, globally optimal solutions. Empirical evidence suggests that the globally optimal solution can return excellent generalization performance in situations where other implementations fail completely. While our current implementation is only applicable to small datasets, we discuss variants that can potentially lead to practically useful algorithms.

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 Dates: 2007-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-19568-2
URI: http://nips.cc/Conferences/2006/
BibTex Citekey: 4146
 Degree: -

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Title: Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006)
Place of Event: Vancouver, BC, Canada
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Title: Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference
Source Genre: Journal
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Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 217 - 224 Identifier: -