nu-Anomica: A Fast Support Vector Based Anomaly Detection Technique
In this paper we propose $\nu$-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In $\nu$-Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard one-class Support Vector Machines while reducing both the training time and the test time by 5-20 times.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[ "026:00" ] |
| contactPoint |
{ "fn": "Nikunj Oza", "@type": "vcard:Contact", "hasEmail": "mailto:Nikunj.C.Oza@nasa.gov" } |
| description | In this paper we propose $\nu$-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In $\nu$-Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard one-class Support Vector Machines while reducing both the training time and the test time by 5-20 times. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "dabh09.pdf", "format": "PDF", "mediaType": "application/pdf", "description": "dabh09.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/dabh09.pdf" } ] |
| identifier | DASHLINK_554 |
| issued | 2012-03-12 |
| keyword |
[ "ames", "dashlink", "nasa" ] |
| landingPage | https://c3.nasa.gov/dashlink/resources/554/ |
| modified | 2025-03-31 |
| programCode |
[ "026:029" ] |
| publisher |
{ "name": "Dashlink", "@type": "org:Organization" } |
| title | nu-Anomica: A Fast Support Vector Based Anomaly Detection Technique |
