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SODA
2001
ACM
79views Algorithms» more  SODA 2001»
15 years 7 months ago
Learning Markov networks: maximum bounded tree-width graphs
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
David R. Karger, Nathan Srebro
UAI
2004
15 years 7 months ago
Convolutional Factor Graphs as Probabilistic Models
Based on a recent development in the area of error control coding, we introduce the notion of convolutional factor graphs (CFGs) as a new class of probabilistic graphical models. ...
Yongyi Mao, Frank R. Kschischang, Brendan J. Frey
JCO
2010
101views more  JCO 2010»
15 years 4 months ago
Separator-based data reduction for signed graph balancing
Abstract Polynomial-time data reduction is a classical approach to hard graph problems. Typically, particular small subgraphs are replaced by smaller gadgets. We generalize this ap...
Falk Hüffner, Nadja Betzler, Rolf Niedermeier
ECCV
2006
Springer
16 years 8 months ago
Statistical Priors for Efficient Combinatorial Optimization Via Graph Cuts
Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...
Daniel Cremers, Leo Grady
SAC
2010
ACM
16 years 1 months ago
Estimating node similarity from co-citation in a spatial graph model
Co-citation (number of nodes linking to both of a given pair of nodes) is often used heuristically to judge similarity between nodes in a complex network. We investigate the relat...
Jeannette Janssen, Pawel Pralat, Rory Wilson