Transductive Support Vector Machines for Structured Variables

Zien, A; Brefeld, U; Scheffer, T

doi:10.1145/1273496.1273645

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Transductive Support Vector Machines for Structured Variables

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Zien, A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CD6F-3

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.