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Hidden conditional random fields
Ariadna Quattoni1, Sybor Wang, Louis-Philippe Morency
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139-4309, USA. ariadna@csail.mit.edu
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 19, 2007
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.
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Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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