Exponential Families for Conditional Random Fields
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.
Cite
Text
Altun et al. "Exponential Families for Conditional Random Fields." Conference on Uncertainty in Artificial Intelligence, 2004.Markdown
[Altun et al. "Exponential Families for Conditional Random Fields." Conference on Uncertainty in Artificial Intelligence, 2004.](https://mlanthology.org/uai/2004/altun2004uai-exponential/)BibTeX
@inproceedings{altun2004uai-exponential,
title = {{Exponential Families for Conditional Random Fields}},
author = {Altun, Yasemin and Smola, Alexander J. and Hofmann, Thomas},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2004},
pages = {2-9},
url = {https://mlanthology.org/uai/2004/altun2004uai-exponential/}
}