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Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces

2007

Book Chapter

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In this paper we study the problem of estimating conditional probability distributions for structured output prediction tasks in Reproducing Kernel Hilbert Spaces. More specically, we prove decomposition results for undirected graphical models, give constructions for kernels, and show connections to Gaussian Process classi- cation. Finally we present ecient means of solving the optimization problem and apply this to label sequence learning. Experiments on named entity recognition and pitch accent prediction tasks demonstrate the competitiveness of our approach.

Author(s): Altun, Y. and Smola, AJ.
Book Title: Predicting Structured Data
Pages: 283-300
Year: 2007
Month: September
Day: 0

Series: Advances in neural information processing systems
Editors: BakIr, G. H., T. Hofmann, B. Sch{\"o}lkopf, A. J. Smola, B. Taskar, S. V.N. Vishwanathan
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Book Chapter (inbook)

Address: Cambridge, MA, USA
Digital: 0
ISBN: 978-0-262-02617-8
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@inbook{5702,
  title = {Density Estimation of Structured Outputs in Reproducing Kernel Hilbert Spaces},
  author = {Altun, Y. and Smola, AJ.},
  booktitle = {Predicting Structured Data},
  pages = {283-300},
  series = {Advances in neural information processing systems},
  editors = {BakIr, G. H., T. Hofmann, B. Sch{\"o}lkopf, A. J. Smola, B. Taskar, S. V.N. Vishwanathan},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = sep,
  year = {2007},
  month_numeric = {9}
}