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Phylogenetic dependency networks: Inferring patterns of adaptation in HIV
, 2009
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Structure search and stability enhancement of Bayesian networks
 Proc. Third IEEE Int’l Conf. Data Mining
, 2003
"... Learning Bayesian network structure from largescale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements to automatically learn Bayesian network structure from data. (1) We propose a linear parent search method to generate ca ..."
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Learning Bayesian network structure from largescale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements to automatically learn Bayesian network structure from data. (1) We propose a linear parent search method to generate candidate graph. (2) We propose a comprehensive approach to eliminate cycles using minimal likelihood loss, a short cycle first heuristic, and a cutedge repairing. (3) We propose structure perturbation to assess the stability of the network and a stabilityimprovement method to refine the network structure. The algorithms are easy to implement and efficient for large networks. Experimental results on two data sets show that our new approach outperforms existing methods. 1.
ClosedForm Learning of Markov Networks from Dependency Networks
"... Markov networks (MNs) are a powerful way to compactly represent a joint probability distribution, but most MN structure learning methods are very slow, due to the high cost of evaluating candidates structures. Dependency networks (DNs) represent a probability distribution as a set of conditional pro ..."
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Cited by 3 (2 self)
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Markov networks (MNs) are a powerful way to compactly represent a joint probability distribution, but most MN structure learning methods are very slow, due to the high cost of evaluating candidates structures. Dependency networks (DNs) represent a probability distribution as a set of conditional probability distributions. DNs are very fast to learn, but the conditional distributions may be inconsistent with each other and few inference algorithms support DNs. In this paper, we present a closedform method for converting a DN into an MN, allowing us to enjoy both the efficiency of DN learning and the convenience of the MN representation. When the DN is consistent, this conversion is exact. For inconsistent DNs, we present averaging methods that significantly improve the approximation. In experiments on 12 standard datasets, our methods are orders of magnitude faster than and often more accurate than combining conditional distributions using weight learning. 1
S.: Directed cycles in Bayesian belief networks: probabilistic semantics and consistency checking complexity
 MICAI 2005: Advances in Artificial Intelligence
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624 Learning with Knowledge from Multiple Experts
"... The use of domain knowledge in a learner can greatly improve the models it produces. However, highquality expert knowledge is very difficult to obtain. Traditionally, researchers have assumed that knowledge comes from a single selfconsistent source. A littleexplored but often more feasible altern ..."
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The use of domain knowledge in a learner can greatly improve the models it produces. However, highquality expert knowledge is very difficult to obtain. Traditionally, researchers have assumed that knowledge comes from a single selfconsistent source. A littleexplored but often more feasible alternative is to use multiple weaker sources. In this paper we take a step in this direction by developing a method for learning the structure of a Bayesian network from multiple experts. Data is then used to refine the structure and estimate parameters. A simple analysis shows that even relatively few noisy experts can produce highquality knowledge when combined. Experiments with real and simulated experts in a variety of domains show the benefits of this approach. 1.
Learning with Knowledge from Multiple Experts
 In ICML 20
, 2003
"... The use of domain knowledge in a learner can greatly improve the models it produces. ..."
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The use of domain knowledge in a learner can greatly improve the models it produces.