Parameter Learning and Convergent Inference for Dense Random Fields.
Philipp KrähenbühlVladlen KoltunPublished in: ICML (3) (2013)
Keyphrases
- parameter learning
- random fields
- conditional random fields
- markov random field
- parameter estimation
- probabilistic model
- structure learning
- bayesian networks
- generative model
- gibbs sampler
- graphical models
- higher order
- hidden markov models
- maximum entropy
- probabilistic graphical models
- maximum likelihood
- structured prediction
- pairwise
- information extraction
- approximate inference
- statistical learning
- em algorithm
- graph cuts
- expectation maximization
- exact inference
- least squares
- image segmentation
- data mining
- similarity measure
- segmentation method
- energy function
- social networks
- message passing
- belief propagation