Learning by extrapolation from marginal to full-multivariate probability distributions: Decreasingly naive Bayesian classification

DOI10.1007/s10994-011-5263-6zbMath1238.68136OpenAlexW2086338626WikidataQ62833566 ScholiaQ62833566MaRDI QIDQ420911

Kai Ming Ting, Houssam Salem, Janice R. Boughton, Fei Zheng, Geoffrey I. Webb

Publication date: 23 May 2012

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-011-5263-6

zbMATH Keywords

Bayesian learningprobabilistic learningnaive Bayesensemble learningclassification learningaveraged one-dependence estimatorsfeatinglearning without model selectionsemi-naive Bayesian learning

Mathematics Subject Classification ID

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

Related Items (6)

Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes ⋮ \(\text{ALR}^n\): accelerated higher-order logistic regression ⋮ Subsumption resolution: an efficient and effective technique for semi-naive Bayesian learning ⋮ Efficient parameter learning of Bayesian network classifiers ⋮ Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis ⋮ When is the naive Bayes approximation not so naive?

Cites Work

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• Classifying under computational resource constraints: anytime classification using probabilistic estimators
• Bayesian network classifiers
• Not so naive Bayes: Aggregating one-dependence estimators
• Random forests

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