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» First-order probabilistic inference
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ECCV
2010
Springer
13 years 10 months ago
Object Recognition with Hierarchical Stel Models
Abstract. We propose a new generative model, and a new image similarity kernel based on a linked hierarchy of probabilistic segmentations. The model is used to efficiently segment ...
Alessandro Perina, Nebojsa Jojic, Umberto Castella...
ML
2006
ACM
131views Machine Learning» more  ML 2006»
13 years 10 months ago
Markov logic networks
We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge b...
Matthew Richardson, Pedro Domingos
IJAR
2010
152views more  IJAR 2010»
13 years 8 months ago
Structural-EM for learning PDG models from incomplete data
Probabilistic Decision Graphs (PDGs) are a class of graphical models that can naturally encode some context specific independencies that cannot always be efficiently captured by...
Jens D. Nielsen, Rafael Rumí, Antonio Salme...
FLAIRS
2009
13 years 7 months ago
Constraint-based Approach to Discovery of Inter Module Dependencies in Modular Bayesian Networks
This paper introduces an information theoretic approach to verification of modular causal probabilistic models. We assume systems which are gradually extended by adding new functi...
Patrick de Oude, Gregor Pavlin
JMLR
2010
218views more  JMLR 2010»
13 years 4 months ago
Simple Exponential Family PCA
Bayesian principal component analysis (BPCA), a probabilistic reformulation of PCA with Bayesian model selection, is a systematic approach to determining the number of essential p...
Jun Li, Dacheng Tao