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  Transductive Support Vector Machines for Structured Variables

Zien, A., Brefeld, U., & Scheffer, T. (2007). Transductive Support Vector Machines for Structured Variables. In Z. Ghahramani (Ed.), ICML '07: 24th International Conference on Machine Learning (pp. 1183-1190). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CD6F-3 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-E2EA-6
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1273496.1273645 (Publisher version)
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 Creators:
Zien, A1, 2, Author           
Brefeld, U, Author
Scheffer, T, 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 study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding non-convex, combinatorial, constrained optimization problems
into continuous, unconstrained optimization
problems. The discrete optimization parameters are eliminated and the resulting differentiable problems can be optimized efficiently. We study the effectiveness of the generalized TSVM on multiclass classification and label-sequence learning problems empirically.

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 Dates: 2007-06
 Publication Status: Issued
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 Rev. Type: -
 Identifiers: DOI: 10.1145/1273496.1273645
BibTex Citekey: 4470
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Title: 24th International Conference on Machine Learning (ICML 2007)
Place of Event: Corvallis, OR, USA
Start-/End Date: 2007-06-20 - 2007-06-24

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Title: ICML '07: 24th International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Ghahramani, Z, Editor
Affiliations:
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1183 - 1190 Identifier: ISBN: 978-1-59593-793-3