8th European Conference on Speech Communication and Technology

Geneva, Switzerland
September 1-4, 2003


Large Margin Methods for Label Sequence Learning

Yasemin Altun, Thomas Hofmann

Brown University, USA

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.

Full Paper

Bibliographic reference.  Altun, Yasemin / Hofmann, Thomas (2003): "Large margin methods for label sequence learning", In EUROSPEECH-2003, 993-996.