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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.
INDEX TERMS
object recognition, model, supervised learning, classification
CITATION
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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