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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, Louis-Philippe Morency, Trevor Darrell, Michael Collins, Sybor Wang, "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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