Transductive support vector machines for structured variables

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Authors

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.

Original languageEnglish
Title of host publicationProceedings of the 24th international conference on Machine learning
Number of pages8
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Publication date2007
Pages1183-1190
ISBN (print)978-1-59593-793-3
DOIs
Publication statusPublished - 2007
Externally publishedYes
EventACM International Conference Proceeding Series - AICPS 2007 - Corvallis, United States
Duration: 20.06.200724.06.2007

DOI