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ICML
2007
IEEE
14 years 8 months ago
Piecewise pseudolikelihood for efficient training of conditional random fields
Discriminative training of graphical models can be expensive if the variables have large cardinality, even if the graphical structure is tractable. In such cases, pseudolikelihood...
Charles A. Sutton, Andrew McCallum
CVPR
2008
IEEE
14 years 9 months ago
Structure learning in random fields for heart motion abnormality detection
Coronary Heart Disease can be diagnosed by assessing the regional motion of the heart walls in ultrasound images of the left ventricle. Even for experts, ultrasound images are dif...
Glenn Fung, Kevin Murphy, Mark Schmidt, Róm...
PAMI
2008
119views more  PAMI 2008»
13 years 7 months ago
Triplet Markov Fields for the Classification of Complex Structure Data
We address the issue of classifying complex data. We focus on three main sources of complexity, namely, the high dimensionality of the observed data, the dependencies between these...
Juliette Blanchet, Florence Forbes
AAAI
2008
13 years 10 months ago
Constrained Classification on Structured Data
Most standard learning algorithms, such as Logistic Regression (LR) and the Support Vector Machine (SVM), are designed to deal with i.i.d. (independent and identically distributed...
Chi-Hoon Lee, Matthew R. G. Brown, Russell Greiner...
ICRA
2009
IEEE
188views Robotics» more  ICRA 2009»
13 years 5 months ago
Onboard contextual classification of 3-D point clouds with learned high-order Markov Random Fields
Contextual reasoning through graphical models such as Markov Random Fields often show superior performance against local classifiers in many domains. Unfortunately, this performanc...
Daniel Munoz, Nicolas Vandapel, Martial Hebert