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Approximating the maximum weighted decomposable graph problem with applications to probabilistic graphical models.
Aritz Pérez
Christian Blum
José Antonio Lozano
Published in:
PGM (2018)
Keyphrases
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probabilistic graphical models
markov networks
graphical models
first order logic
belief propagation
maximum likelihood
bayesian networks
exact inference
probabilistic model
approximate inference
latent variables
maximum margin
hidden variables
markov random field
parameter learning
special case
conditional random fields
neural network
probabilistic inference
document classification
posterior probability
directed acyclic graph
soft computing
machine learning
knowledge representation
computational intelligence
higher order