Learning to detect incidents from noisily labeled data

DOI10.1007/s10994-009-5141-7zbMath1470.68178OpenAlexW2087751936MaRDI QIDQ1959586

Tomáš Šingliar, Miloš Hauskrecht

Publication date: 7 October 2010

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

Full work available at URL: https://doi.org/10.1007/s10994-009-5141-7

zbMATH Keywords

dynamic Bayesian networksroad networkSVMincident detection

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Traffic problems in operations research (90B20) Probabilistic graphical models (62H22)

Related Items

Learning to detect incidents from noisily labeled data, An ensemble method for concept drift in nonstationary environment

Uses Software

• BNT

Cites Work

• Bayesian network classifiers
• Learning to detect incidents from noisily labeled data
• 10.1162/15324430152748218
• Approximating discrete probability distributions with dependence trees
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