Learning Bayesian network classifiers by risk minimization

DOI10.1016/J.IJAR.2011.10.006zbMath1242.68230OpenAlexW2055685281MaRDI QIDQ432990

Boaz Lerner, Roy Kelner

Publication date: 5 July 2012

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2011.10.006

zbMATH Keywords

classificationBayesian networksprobabilistic graphical modelsstructure learning

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Reducing the structure space of Bayesian classifiers using some general algorithms ⋮ Learning continuous time Bayesian network classifiers ⋮ Design of radial basis function neural network classifier realized with the aid of data preprocessing techniques: design and analysis ⋮ Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier

Uses Software

• UCI-ml
• LIBSVM
• BNT
• PRTools
• MLC++

Cites Work

• Feature selection for Bayesian network classifiers using the MDL-FS score
• Boosted Bayesian network classifiers
• Wrappers for feature subset selection
• A Bayesian method for the induction of probabilistic networks from data
• Bayesian network classifiers
• Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers
• 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
• Support-vector networks
• Bayesian network classifiers versus selective \(k\)-NN classifier
• Knowledge representation and inference in similarity networks and Bayesian multinets
• 10.1162/153244301753344605
• Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach
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