Exponential Families for Conditional Random Fields

Altun, Y; Smola, AJ; Hofmann, T

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Exponential Families for Conditional Random Fields

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Hofmann, T
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://dl.acm.org/citation.cfm?id=1036844
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Citation

Altun, Y., Smola, A., & Hofmann, T. (2004). Exponential Families for Conditional Random Fields. In C. Meek, M. Chickering, & J. Halpern (Eds.), UAI '04: 20th Annual Conference on Uncertainty in Artificial Intelligence (pp. 2-9). New York, NY, USA: ACM Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D8A3-3

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.