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ICML
2010
IEEE
13 years 11 months ago
Bottom-Up Learning of Markov Network Structure
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...
Jesse Davis, Pedro Domingos
CORR
2006
Springer
86views Education» more  CORR 2006»
13 years 10 months ago
Optimal Distortion-Power Tradeoffs in Sensor Networks: Gauss-Markov Random Processes
We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements ...
Nan Liu, Sennur Ulukus
IJSNET
2007
155views more  IJSNET 2007»
13 years 9 months ago
Distributed Bayesian fault diagnosis of jump Markov systems in wireless sensor networks
: A Bayesian distributed online change detection algorithm is proposed for monitoring a dynamical system by a wireless sensor network. The proposed solution relies on modelling the...
Hichem Snoussi, Cédric Richard
HICSS
2003
IEEE
220views Biometrics» more  HICSS 2003»
14 years 3 months ago
Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks
This paper describes a novel approach using Hidden Markov Models (HMM) to detect complex Internet attacks. These attacks consist of several steps that may occur over an extended pe...
Dirk Ourston, Sara Matzner, William Stump, Bryan H...
ICCV
2003
IEEE
14 years 11 months ago
Tracking Articulated Body by Dynamic Markov Network
A new method for visual tracking of articulated objects is presented. Analyzing articulated motion is challenging because the dimensionality increase potentially demands tremendou...
Ying Wu, Gang Hua, Ting Yu