SODA
2001
ACM
13 years 7 months ago
2001
ACM
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
SDM
2008
SIAM
13 years 7 months ago
2008
SIAM
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
ECML
2006
Springer
13 years 9 months ago
2006
Springer
Abstract. We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend na
ECCV
2010
Springer
13 years 11 months ago
2010
Springer
We present a data-driven approach to predict the importance of edges and construct a Markov network for image analysis based on statistical models of global and local image feature...
SIGECOM
2003
ACM
13 years 11 months ago
2003
ACM
We examine correlated equilibria in the recently introduced formalism of graphical games, a succinct representation for multiplayer games. We establish a natural and powerful rela...
3DPVT
2006
IEEE
13 years 12 months ago
2006
IEEE
We present an algorithm for automatic locating of anthropometric landmarks on 3D human scans. Our method is based on learning landmark characteristics and the spatial relationship...
ICIP
2007
IEEE
14 years 3 days ago
2007
IEEE
Markov networks are an effective tool for the difficult but important problem of recognizing people in consumer image collections. Given a small set of labeled faces, we seek to ...
ECCV
2002
Springer
14 years 7 months ago
2002
Springer
In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF's). These three MRF's model a smooth fie...