Learning to detect incidents from noisily labeled data

Machine Learning

Volumn 79, Issue 3, 2010, Pages 335-354

  Šingliar, Tomáš ^a   Hauskrecht, Miloš ^a  

^a Learning Research and Development Ctr   (United States)

Author keywords

Dynamic Bayesian networks; Incident detection; Road network; SVM

Indexed keywords

DATA LABELS; DETECTION MODELS; DETECTION PERFORMANCE; DYNAMIC BAYESIAN NETWORK; FALSE POSITIVE; INCIDENT DETECTION; LABELED DATA; MACHINE-LEARNING; MANUAL TUNING; MODEL-BASED; ROAD NETWORK; SVM MODEL; TEMPORAL NOISE; TRAFFIC FLOW; TRAFFIC INCIDENT DETECTOR; TRAFFIC INCIDENTS; TRAFFIC SENSORS; TRAINING DATA;

BAYESIAN NETWORKS; DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INTELLIGENT NETWORKS; LABELS; LEARNING SYSTEMS; MOTOR TRANSPORTATION; ROADS AND STREETS; SENSORS; SPEECH RECOGNITION; TRAFFIC CONTROL; TRAFFIC SURVEYS;

LEARNING ALGORITHMS;

EID: 77953027709     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-009-5141-7     Document Type: Article

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