Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. / Smith, James; Nouretdinov, Ilia; Craddock, Rachel; Offer, Charles; Gammerman, Alex.

Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. ed. / Alex Gammerman; Vladimir Vovk; Harris Papadopoulos. Springer, 2015. p. 281-290 (Lecture Notes in Computer Science; Vol. 9047).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. / Smith, James; Nouretdinov, Ilia; Craddock, Rachel; Offer, Charles; Gammerman, Alex.

Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. ed. / Alex Gammerman; Vladimir Vovk; Harris Papadopoulos. Springer, 2015. p. 281-290 (Lecture Notes in Computer Science; Vol. 9047).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Smith, J, Nouretdinov, I, Craddock, R, Offer, C & Gammerman, A 2015, Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. in A Gammerman, V Vovk & H Papadopoulos (eds), Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Lecture Notes in Computer Science, vol. 9047, Springer, pp. 281-290. https://doi.org/10.1007/978-3-319-17091-6_23

APA

Smith, J., Nouretdinov, I., Craddock, R., Offer, C., & Gammerman, A. (2015). Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. In A. Gammerman, V. Vovk, & H. Papadopoulos (Eds.), Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings (pp. 281-290). (Lecture Notes in Computer Science; Vol. 9047). Springer. https://doi.org/10.1007/978-3-319-17091-6_23

Vancouver

Smith J, Nouretdinov I, Craddock R, Offer C, Gammerman A. Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. In Gammerman A, Vovk V, Papadopoulos H, editors, Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Springer. 2015. p. 281-290. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-17091-6_23

Author

Smith, James ; Nouretdinov, Ilia ; Craddock, Rachel ; Offer, Charles ; Gammerman, Alex. / Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. editor / Alex Gammerman ; Vladimir Vovk ; Harris Papadopoulos. Springer, 2015. pp. 281-290 (Lecture Notes in Computer Science).

BibTeX

@inproceedings{4f5b61e2606a447fbea6a1c4019ddcd3,
title = "Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy",
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.",
author = "James Smith and Ilia Nouretdinov and Rachel Craddock and Charles Offer and Alex Gammerman",
year = "2015",
month = apr,
day = "3",
doi = "10.1007/978-3-319-17091-6_23",
language = "English",
isbn = "978-3-319-17090-9",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "281--290",
editor = "Alex Gammerman and Vladimir Vovk and Harris Papadopoulos",
booktitle = "Statistical Learning and Data Sciences",

}

RIS

TY - GEN

T1 - Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy

AU - Smith, James

AU - Nouretdinov, Ilia

AU - Craddock, Rachel

AU - Offer, Charles

AU - Gammerman, Alex

PY - 2015/4/3

Y1 - 2015/4/3

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-17091-6_23

DO - 10.1007/978-3-319-17091-6_23

M3 - Conference contribution

SN - 978-3-319-17090-9

T3 - Lecture Notes in Computer Science

SP - 281

EP - 290

BT - Statistical Learning and Data Sciences

A2 - Gammerman, Alex

A2 - Vovk, Vladimir

A2 - Papadopoulos, Harris

PB - Springer

ER -