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Exponential Families for Conditional Random Fields
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.
@inproceedings{2741, title = {Exponential Families for Conditional Random Fields}, journal = {Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004)}, 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.}, pages = {2-9}, editors = {Chickering, D.M. , J.Y. Halpern}, publisher = {Morgan Kaufmann}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {San Francisco, CA, USA}, month = jul, year = {2004}, slug = {2741}, author = {Altun, Y. and Smola, AJ. and Hofmann, T.}, month_numeric = {7} }