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  Large Margin Methods for Label Sequence Learning

Altun, Y., & Hofmann, T. (2003). Large Margin Methods for Label Sequence Learning. In 8th European Conference on Speech Communication and Technology (EUROSPEECH 2003) (pp. 993-996). Bonn, Germany: International Speech Communication Association.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DBB7-1 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-70E4-B
Genre: Conference Paper

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https://www.isca-speech.org/archive/archive_papers/eurospeech_2003/e03_0993.pdf (Publisher version)
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Altun, Y1, Author           
Hofmann, T1, Author           
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 Abstract: Label sequence learning is the problem of inferring a state sequence from an observation sequence, where the state sequence may encode a labeling, annotation or segmentation of the sequence. In this paper we give an overview of discriminative methods developed for this problem. Special emphasis is put on large margin methods by generalizing multiclass Support Vector Machines and AdaBoost to the case of label sequences. An experimental evaluation demonstrates the advantages over classical approaches like Hidden Markov Models and the competitiveness with methods like Conditional Random Fields.

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 Dates: 2003-09
 Publication Status: Issued
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 Identifiers: BibTex Citekey: AltunH2003
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Title: 8th European Conference on Speech Communication and Technology (EUROSPEECH 2003)
Place of Event: Genf, Switzerland
Start-/End Date: 2003-09-01 - 2003-09-04

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Title: 8th European Conference on Speech Communication and Technology (EUROSPEECH 2003)
Source Genre: Proceedings
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Publ. Info: Bonn, Germany : International Speech Communication Association
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 993 - 996 Identifier: -