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Exponential Families for Conditional Random Fields

2004

Conference Paper

ei


In this paper we define conditional random fields in reproducing kernel Hilbert spaces and show connections to Gaussian Process classification. More specifically, we prove decomposition results for undirected graphical models and we give constructions for kernels. Finally we present efficient means of solving the optimization problem using reduced rank decompositions and we show how stationarity can be exploited efficiently in the optimization process.

Author(s): Altun, Y. and Smola, AJ. and Hofmann, T.
Journal: Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004)
Pages: 2-9
Year: 2004
Month: July
Day: 0
Editors: Chickering, D.M. , J.Y. Halpern
Publisher: Morgan Kaufmann

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

Event Name: 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004)
Event Place: Banff, Alberta, Canada

Address: San Francisco, CA, USA
Digital: 0
ISBN: 0-9749039-0-6
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{2741,
  title = {Exponential Families for Conditional Random Fields},
  author = {Altun, Y. and Smola, AJ. and Hofmann, T.},
  journal = {Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004)},
  pages = {2-9},
  editors = {Chickering, D.M. , J.Y. Halpern},
  publisher = {Morgan Kaufmann},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {San Francisco, CA, USA},
  month = jul,
  year = {2004},
  month_numeric = {7}
}