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ICMLA
2007
13 years 9 months ago
Learning bayesian networks consistent with the optimal branching
We introduce a polynomial-time algorithm to learn Bayesian networks whose structure is restricted to nodes with in-degree at most k and to edges consistent with the optimal branch...
Alexandra M. Carvalho, Arlindo L. Oliveira
ECAI
2006
Springer
13 years 11 months ago
Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?
One problem faced in knowledge engineering for Bayesian networks is the exponential growth of the number of parameters in their conditional probability tables (CPTs). The most comm...
Adam Zagorecki, Marek J. Druzdzel
IJCAI
2003
13 years 9 months ago
When Discriminative Learning of Bayesian Network Parameters Is Easy
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
Hannes Wettig, Peter Grünwald, Teemu Roos, Pe...
JMLR
2010
113views more  JMLR 2010»
13 years 2 months ago
Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure
We study the problem of learning an optimal Bayesian network in a constrained search space; skeletons are compelled to be subgraphs of a given undirected graph called the super-st...
Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru M...
AIED
2005
Springer
14 years 1 months ago
Tradeoff analysis between knowledge assessment approaches
Abstract: The problem of modeling and assessing an individual’s ability level is central to learning environments. Numerous approaches exists to this end. Computer Adaptive Testi...
Michel Desmarais, Shunkai Fu, Xiaoming Pu