Classifying under computational resource constraints: anytime classification using probabilistic estimators

DOI10.1007/S10994-007-5020-ZzbMath1470.68203OpenAlexW2011823784WikidataQ62833659 ScholiaQ62833659MaRDI QIDQ1009341

Geoff Webb, Ying Yang, Kai Ming Ting, Kevin B. Korb

Publication date: 31 March 2009

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

Full work available at URL: https://doi.org/10.1007/s10994-007-5020-z

zbMATH Keywords

ensemble methodsprobabilistic predictionBayesian classifiersanytime classificationanytime learning

Mathematics Subject Classification ID

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

Related Items (3)

Learning by extrapolation from marginal to full-multivariate probability distributions: Decreasingly naive Bayesian classification ⋮ Anytime classification for a pool of instances ⋮ A lazy bagging approach to classification

Uses Software

• UCI-ml

Cites Work

• Wrappers for feature subset selection
• Estimating the dimension of a model
• Not so naive Bayes: Aggregating one-dependence estimators
• A new look at the statistical model identification
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