Abstract
The paper investigates the problem of anomaly detection in the maritime trajectory surveillance domain. Conformal predictors in this paper are used as a basis for anomaly detection. A multi-class hierarchy framework is presented for different class representations. Experiments are conducted with data taken from shipping vessel trajectories using data obtained through AIS (Automatic Identification System) broadcasts and the results are discussed.
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Smith, J., Nouretdinov, I., Craddock, R., Offer, C., Gammerman, A. (2015). Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_23
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DOI: https://doi.org/10.1007/978-3-319-17091-6_23
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-17091-6
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