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.