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PKDD
2010
Springer
158views Data Mining» more  PKDD 2010»
13 years 6 months ago
Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach
In this paper, we introduce a simple but efficient greedy algorithm, called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Ga...
Katya Scheinberg, Irina Rish
ICML
2010
IEEE
13 years 8 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
NIPS
2003
13 years 9 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller
CORR
2006
Springer
86views Education» more  CORR 2006»
13 years 7 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 7 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