Uncertainty in Artificial Intelligence
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Exponential Families for Conditional Random Fields
Yasemin Altun, Alex Smola, Thomas Hofmann
Abstract:
In this paper we de ne conditional random elds in reproducing kernel Hilbert spaces and show connections to Gaussian Process classi cation. More speci cally, we prove decomposition results for undirected graphical models and we give constructions for kernels. Finally we present e cient means of solving the optimization problem using reduced rank decompositions and we show how stationarity can be exploited e ciently in the optimization process.
Keywords: null
Pages: 2-9
PS Link:
PDF Link: /papers/04/p2-altun.pdf
BibTex:
@INPROCEEDINGS{Altun04,
AUTHOR = "Yasemin Altun and Alex Smola and Thomas Hofmann",
TITLE = "Exponential Families for Conditional Random Fields",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "2--9"
}


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