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Kanishka Bhaduri

Member since: Sep 24, 2010, Mission Critical Technologies Inc

ν-Anomica: A Fast Support Vector based Novelty Detection Technique

Shared by Kanishka Bhaduri, updated on Nov 17, 2010

Summary

Author(s) :
Santanu Das, Kanishka Bhaduri, Nikunj Oza, Ashok Srivastava
Abstract

In this paper we propose ν-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 ν-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 oneclass Support Vector Machines while reducing both the training time and the test time by 5 − 20 times.

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Publication Name
ν-Anomica: A Fast Support Vector based Novelty Detection Technique
Publication Location
IEEE International Conference on Data Mining (ICDM'09), pp. 101-109
Year Published
2009

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nu_anomica.pdf
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