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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.
object recognition, model, supervised learning, classification
Ariadna Quattoni, Sybor Wang, Louis-Philippe Morency, Michael Collins, Trevor Darrell, "Hidden Conditional Random Fields", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 1848-1852, October 2007, doi:10.1109/TPAMI.2007.1124
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