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https://www.researchgate.net/publication/224142557_Anomaly_Detection_Through_a_Bayesian_Support_Vector_Machine
Support Vector Machine (SVM) is another classification algorithm widely used in anomaly detection systems, in the case of numerical data [62], [92], [104], [130]. SVM creates a boundary between ...
https://ieeexplore.ieee.org/document/5475445/
Anomaly Detection Through a Bayesian Support Vector Machine Abstract: This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anomalies, and trend output classification probabilities, as a way to monitor the health of a system. In the absence of “unhealthy” (negative class) information ...Cited by: 116
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.457.989
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract—This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anoma-lies, and trend output classification probabilities, as a way to mon-itor the health of a system. In the absence of “unhealthy ” (nega-tive class) information, a marginal kernel density ...
https://web.calce.umd.edu/articles/abstracts/2010/AnomalyDetection_Bayesion_abstract.html
Anomaly Detection Through a Bayesian Support Vector Machine. Vasilis A. Sotiris Center for Advanced Life Cycle Engineering (CALCE) University of Maryland College Park, MD 20742 USA. Peter Tse City University in Hong Kong Kowloon, Hong Kong. Michael Pecht, Member, IEEE City University in Hong Kong Kowloon, Hong Kong and
https://www.prognostics.umd.edu/calcepapers/10_Vasilis_AnamolyDetectionBayesianSupportVector.pdf
Anomaly Detection Through a Bayesian Support Vector Machine Vasilis A. Sotiris, Peter Tse and Michael Pecht, member, IEEE Abstract—This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anomalies, and trend output classification probabilities, as a way to monitor the health of a system.
http://www.math.umd.edu/~rvbalan/TEACHING/AMSC663Fall2007/PROJECTS/P4/Anomaly%20Detection%20Through%20a%20Bayesian%20SVM_final%20Report.pdf
Anomaly Detection Through a Bayesian Support Vector Machine Vasilis A. Sotiris Department of Mathematics PhD Candidate in Applied Mathematics and Scientific Computation University of Maryland, College Park, MD [email protected] Michael Pecht Department of Mechanical Engineering Director of the Center for Advanced Life Cycle Engineering (CALCE)
https://www.academia.edu/15386043/Anomaly_Detection_Through_a_Bayesian_Support_Vector_Machine
Anomaly Detection Through a Bayesian Support Vector Machine
https://en.wikipedia.org/wiki/Support-vector_machine
Support-vector machine weights have also been used to interpret SVM models in the past. Posthoc interpretation of support-vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences. History
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