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  Support vector machine learning for interdependent and structured output spaces

Tsochantaridis, I., Hofmann, T., Joachims, T., & Altun, Y. (2004). Support vector machine learning for interdependent and structured output spaces. In R. Greiner, & D. Schuurmans (Eds.), ICML '04: Twenty-first International Conference on Machine Learning (pp. 823-830). New York, NY, USA: ACM Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D8AB-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-52CC-9
Genre: Conference Paper

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 Creators:
Tsochantaridis, I, Author
Hofmann, T1, Author              
Joachims, T, Author
Altun, Y1, Author              
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs such as multiple dependent output variables and structured output spaces. We propose to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs. The resulting optimization problem is solved efficiently by a cutting plane algorithm that exploits the sparseness and structural decomposition of the problem. We demonstrate the versatility and effectiveness of our method on problems ranging from supervised grammar learning and named-entity recognition, to taxonomic text classification and sequence alignment.

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 Dates: 2004-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1015330.1015341
BibTex Citekey: TsochantaridisHJA2004
 Degree: -

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Title: Twenty-first International Conference on Machine Learning (ICML 2004)
Place of Event: Banff, Canada
Start-/End Date: 2004-07-04 - 2004-07-08

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Title: ICML '04: Twenty-first International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Greiner, R, Editor
Schuurmans, D, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: 104 Start / End Page: 823 - 830 Identifier: ISBN: 1-58113-838-5