Header logo is

Transductive Support Vector Machines for Structured Variables

2007

Conference Paper

ei


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.
Book Title: ICML 2007
Journal: Proceedings of the 24th International Conference on Machine Learning (ICML 2007)
Pages: 1183-1190
Year: 2007
Month: June
Day: 0
Editors: Ghahramani, Z.
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1273496.1273645
Event Name: 24th International Conference on Machine Learning
Event Place: Corvallis, OR, USA

Address: New York, NY, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
Web

BibTex

@inproceedings{4470,
  title = {Transductive Support Vector Machines for Structured Variables},
  author = {Zien, A. and Brefeld, U. and Scheffer, T.},
  journal = {Proceedings of the 24th International Conference on Machine Learning (ICML 2007)},
  booktitle = {ICML 2007},
  pages = {1183-1190},
  editors = {Ghahramani, Z. },
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2007},
  month_numeric = {6}
}