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Learning bayesian networks from Markov random fields: An efficient algorithm for linear models
Zhenxing Wang, Laiwan Chan
Article No.: 10
Dependency analysis is a typical approach for Bayesian network learning, which infers the structures of Bayesian networks by the results of a series of conditional independence (CI) tests. In practice, testing independence conditioning on large...
ciForager: Incrementally discovering regions of correlated change in evolving graphs
Jeffrey Chan, James Bailey, Christopher Leckie, Michael Houle
Article No.: 11
Data mining techniques for understanding how graphs evolve over time have become increasingly important. Evolving graphs arise naturally in diverse applications such as computer network topologies, multiplayer games and medical imaging. A natural...
Comparative document summarization via discriminative sentence selection
Dingding Wang, Shenghuo Zhu, Tao Li, Yihong Gong
Article No.: 12
Given a collection of document groups, a natural question is to identify the differences among them. Although traditional document summarization techniques can summarize the content of the document groups one by one, there exists a great necessity...
Forecasting in the NBA and other team sports: Network effects in action
Pedro O. S. Vaz de Melo, Virgilio A. F. Almeida, Antonio A. F. Loureiro, Christos Faloutsos
Article No.: 13
The multi-million sports-betting market is based on the fact that the task of predicting the outcome of a sports event is very hard. Even with the aid of an uncountable number of descriptive statistics and background information, only a few can...