Hidden conditional random fields

IEEE Trans Pattern Anal Mach Intell. 2007 Oct;29(10):1848-53. doi: 10.1109/TPAMI.2007.1124.


We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state Conditional Random Field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time.

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Markov Chains
  • Models, Statistical
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity