Score-based methods for learning Markov boundaries by searching in constrained spaces
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Publication:1944976
DOI10.1007/s10618-011-0247-5zbMath1260.68318OpenAlexW2156050561MaRDI QIDQ1944976
Moisés Fernández, Silvia Acid, Luis M. de Campos
Publication date: 28 March 2013
Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10618-011-0247-5
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Cites Work
- The max-min hill-climbing Bayesian network structure learning algorithm
- Towards scalable and data efficient learning of Markov boundaries
- Bayesian network learning algorithms using structural restrictions
- Selection of relevant features and examples in machine learning
- Wrappers for feature subset selection
- Causation, prediction, and search
- A Bayesian method for the induction of probabilistic networks from data
- Estimating the dimension of a model
- Bayesian network classifiers
- Adaptive probabilistic networks with hidden variables
- Bayesian learning in social networks.
- A new approach for learning belief networks using independence criteria
- On discriminative Bayesian network classifiers and logistic regression
- Learning Bayesian network classifiers: Searching in a space of partially directed acyclic graphs
- Learning Bayesian networks: The combination of knowledge and statistical data
- A Fast Hill-Climbing Algorithm for Bayesian Networks Structure Learning
- 10.1162/153244302760200696
- 10.1162/153244303322753616
- Gene selection for cancer classification using support vector machines
- A new look at the statistical model identification
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