{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Nikunj Oza", "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", "description": "dabh09.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/dabh09.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "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": {"@type": "org:Organization", "name": "Dashlink"}, "title": "nu-Anomica: A Fast Support Vector Based Anomaly Detection Technique"}