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  Exponential Families for Conditional Random Fields

Altun, Y., Smola, A., & Hofmann, T. (2004). Exponential Families for Conditional Random Fields. In 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004) (pp. 2-9). San Francisco, CA, USA: Morgan Kaufmann.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D8A3-3 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D8A4-1
Genre: Conference Paper

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 Creators:
Altun, Y1, Author           
Smola, AJ, Author
Hofmann, T1, Author           
Chickering J.Y. Halpern, D.M., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: 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.

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 Dates: 2004-07
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-9749039-0-6
URI: http://portal.acm.org/citation.cfm?id=1036844
BibTex Citekey: 2741
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Title: 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004)
Place of Event: Banff, Alberta, Canada
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Title: 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004)
Source Genre: Proceedings
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Publ. Info: San Francisco, CA, USA : Morgan Kaufmann
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2 - 9 Identifier: -