PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Exponential Families for Conditional Random Fields
Yasemin Altun, Alex Smola and Thomas Hofmann
In: Uncertainty in Artificial Intelligence, Jul 2004, Banff.

Abstract

In this paper we define conditional random fields in Hilbert space and we 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.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
Theory & Algorithms
ID Code:855
Deposited By:Adam Kowalczyk
Deposited On:02 January 2005