Using Support Vector Machines For Anomalous Change Detection

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Using support vector machines for anomalous change ...

    https://www.researchgate.net/publication/224200478_Using_support_vector_machines_for_anomalous_change_detection
    We cast anomalous change detection as a binary classification problem, and use a support vector machine (SVM) to build a detector that does not depend on …

USING SUPPORT VECTOR MACHINES FOR ANOMALOUS …

    http://geodesy.unr.edu/hanspeterplag/library/IGARSS2010/pdfs/1453.pdf
    USING SUPPORT VECTOR MACHINES FOR ANOMALOUS CHANGE DETECTION Ingo Steinwart, James Theiler, and Daniel Llamocca Los Alamos National Laboratory Los Alamos, NM 87545 1. INTRODUCTION Given two images of the same scene, taken at different times and (inevitably) under different conditions, we consider the problem of nding anomalous changes …

ANOMALOUS TRAJECTORY DETECTION USING SUPPORT …

    https://www.researchgate.net/profile/Gl_Foresti/publication/232636648_Anomalous_trajectory_detection_using_support_vector_machines/links/55cdf95908ae6a881381b101.pdf
    ANOMALOUS TRAJECTORY DETECTION USING SUPPORT VECTOR MACHINES C. Piciarelli, G. L. Foresti Department of Mathematics and Computer Science University of UdineCited by: 16

Using support vector machines for anomalous change ...

    https://www.osti.gov/servlets/purl/1022064
    We cast anomalous change detection as a binary classification problem, and use a support vector machine (SVM) to build a detector that does not depend on assumptions about the underlying data distribution. To speed up the computation, our SVM is implemented, in part, on a graphical processing unit ...

Robust Anomaly Detection Using Support Vector Machines

    http://www.cs.unc.edu/~jeffay/courses/nidsS05/ai/robust-anomaly-detection-using.pdf
    Robust Anomaly Detection Using Support Vector Machines Wenjie Hu Yihua Liao V. Rao Vemuri Department of Applied Science Department of Computer Science Department of Applied Science University of California, Davis University of California, Davis University of California, Davis [email protected] [email protected] [email protected]

One Class Support Vector Machines for Detecting Anomalous ...

    http://www2.stat.duke.edu/~kheller/ocsvmpr.pdf
    One Class Support Vector Machines for Detecting Anomalous Windows Registry Accesses Katherine A. Heller Krysta M. Svore Angelos D. Keromytis Salvatore J. Stolfo Dept. of Computer Science Columbia University 1214 Amsterdam Avenue New York, NY 10025 heller,kmsvore,angelos,sal @cs.columbia.edu Abstract We present a new Host-based …

USING SUPPORT VECTOR MACHINES FOR LANE-CHANGE …

    https://www.cs.drexel.edu/~salvucci/publications/Mandalia-HFES05.pdf
    USING SUPPORT VECTOR MACHINES FOR LANE-CHANGE DETECTION Hiren M. Mandalia Dario D. Salvucci Drexel University Drexel University Philadelphia, PA Philadelphia, PA Driving is a complex task that requires constant attention, and intelligent transportation systems that

Improving Anomalous Rare Attack Detection ... - SpringerLink

    https://link.springer.com/article/10.1007/s11063-015-9457-y
    Jul 11, 2015 · Hence, in this letter, we proposed a new classifier to improve the anomalous attacks detection rate based on support vector machine (SVM) and genetic programming (GP). Based on the experimental results, our classifier, GPSVM, managed to get higher detection rate on the anomalous rare attacks, without significant reduction on the overall accuracy.Cited by: 10

Using support vector machines to improve elemental ion ...

    https://www.osti.gov/pages/biblio/1213439
    We cast anomalous change detection as a binary classification problem, and use a support vector machine (SVM) to build a detector that does not depend on assumptions about the underlying data distribution. To speed up the computation, our SVM is implemented, in part, on a graphical processing unit.

Anomaly detection in earth dam and levee passive seismic ...

    https://www.sciencedirect.com/science/article/pii/S1877750316304185
    Our research thus far proves internal erosion events are separable in earth dam and levee (EDL) passive seismic data using unsupervised clustering and crack detection using support vector machines . In this paper, we continue development of an automatic anomaly detection scheme.Cited by: 25



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