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SODA
2001
ACM
79views Algorithms» more  SODA 2001»
9 years 20 days ago
Learning Markov networks: maximum bounded tree-width graphs
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 ...
David R. Karger, Nathan Srebro
SDM
2008
SIAM
138views Data Mining» more  SDM 2008»
9 years 21 days ago
Learning Markov Network Structure using Few Independence Tests
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...
Parichey Gandhi, Facundo Bromberg, Dimitris Margar...
ECML
2006
Springer
9 years 3 months ago
Bayesian Learning of Markov Network Structure
Abstract. We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend na
Aleks Jakulin, Irina Rish
ECCV
2010
Springer
9 years 4 months ago
Learning What and How of Contextual Models for Scene Labeling
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
134views ECommerce» more  SIGECOM 2003»
9 years 4 months ago
Correlated equilibria in graphical games
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...
Sham Kakade, Michael J. Kearns, John Langford, Lui...
3DPVT
2006
IEEE
227views Visualization» more  3DPVT 2006»
9 years 5 months ago
Automatic Locating of Anthropometric Landmarks on 3D Human Models
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...
Zouhour Ben Azouz, Chang Shu, Anja Mantel
ICIP
2007
IEEE
9 years 5 months ago
Using a Markov Network to Recognize People in Consumer Images
Markov networks are an effective tool for the dif´Čücult but important problem of recognizing people in consumer image collections. Given a small set of labeled faces, we seek to ...
Andrew C. Gallagher, Tsuhan Chen
ECCV
2002
Springer
10 years 1 months ago
Stereo Matching Using Belief Propagation
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...
Jian Sun, Heung-Yeung Shum, Nanning Zheng
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