Anomica: Fast Support Vector Based Novelty Detection

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.