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nu-Anomica: A Fast Support Vector Based Novelty Detection TechniqueIn 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 -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.
Document ID
20100023451
Document Type
Conference Paper
Authors
Das, Santanu (California Univ. Santa Cruz, CA, United States)
Bhaduri, Kanishka (Mission Critical Technologies, Inc. Moffett Field, CA, United States)
Oza, Nikunj C. (NASA Ames Research Center Moffett Field, CA, United States)
Srivastava, Ashok N. (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 24, 2013
Publication Date
December 16, 2009
Subject Category
Mathematical and Computer Sciences (General)
Report/Patent Number
ARC-E-DAA-TN887
Meeting Information
2009 IEEE International Conference on Data Mining (ICDM 2009)(Miami, FL)
Funding Number(s)
CONTRACT_GRANT: NAS2-03144
CONTRACT_GRANT: NNA08CG83C
Distribution Limits
Public
Copyright
Public Use Permitted.

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