15 search results - page 3 / 3 » Constrained Approximate Maximum Entropy Learning of Markov R... |
KDD
2006
ACM
14 years 7 months ago
2006
ACM
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
Learning undirected graphical models such as Markov random fields is an important machine learning task with applications in many domains. Since it is usually intractable to learn...
CVPR
2008
IEEE
14 years 9 months ago
2008
IEEE
Prior distributions are useful for robust low-level vision, and undirected models (e.g. Markov Random Fields) have become a central tool for this purpose. Though sometimes these p...
JMLR
2008
13 years 7 months ago
2008
The max-sum classifier predicts n-tuple of labels from n-tuple of observable variables by maximizing a sum of quality functions defined over neighbouring pairs of labels and obser...
CVPR
2011
IEEE
13 years 2 months ago
2011
IEEE
This paper presents a novel intrinsic 3D surface distance and its use in a complete probabilistic tracking framework for dynamic 3D data. Registering two frames of a deforming 3D ...