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Transductive Support Vector Machines for Structured Variables




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.

Author(s): Zien, A. and Brefeld, U. and Scheffer, T.
Year: 2007
Month: June
Day: 0

Department(s): Empirical Inference
Bibtex Type: Talk (talk)

Digital: 0
Event Name: International Conference on Machine Learning (ICML 2007)
Event Place: Corvallis, OR, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Transductive Support Vector Machines for Structured Variables},
  author = {Zien, A. and Brefeld, U. and Scheffer, T.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2007},
  month_numeric = {6}