{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Kanishka Bhaduri", "hasEmail": "mailto:kanishka.bhaduri-1@nasa.gov"}, "description": "In this paper we propose \u03bd-Anomica, a novel\r\nanomaly 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.\r\nIn \u03bd-Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision\r\nplane 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\r\nprocedure closely preserves the accuracy of standard oneclass Support Vector Machines while reducing both the training time and the test time by 5 \u2212 20 times.", "distribution": [{"@type": "dcat:Distribution", "description": "nu_anomica.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/nu_anomica.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "nu_anomica.pdf"}], "identifier": "DASHLINK_259", "issued": "2010-11-17", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/259/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "\u03bd-Anomica: A Fast Support Vector based Novelty Detection Technique"}