Exponential Families for Conditional Random Fields
Machine Learning
2012-07-19 v1 Machine Learning
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
Cite
@article{arxiv.1207.4131,
title = {Exponential Families for Conditional Random Fields},
author = {Yasemin Altun and Alex Smola and Thomas Hofmann},
journal= {arXiv preprint arXiv:1207.4131},
year = {2012}
}
Comments
Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)