title: Hidden conditional random fields.
abstract: 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.
authors: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139-4309, USA. ariadna@csail.mit.edu
authors: Ariadna
authors: A
authors: Quattoni
authors: Sybor
authors: S
authors: Wang
authors: Louis-Philippe
authors: LP
authors: Morency
authors: Michael
authors: M
authors: Collins
authors: Trevor
authors: T
authors: Darrell
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity