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Transductive Support Vector Machines for Structured Variables


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

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Zien, A., Brefeld, U., & Scheffer, T. (2007). Transductive Support Vector Machines for Structured Variables. Talk presented at International Conference on Machine Learning (ICML 2007). Corvallis, OR, USA.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CDA5-7
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.