Constant False Alarm Rate for Online one Class Svm Learning
Abstract
Many one class SVM applications require online learning technique when time series data are encountered. Most of the existing methods for online SVM learning are based on C SVM without adapting the constraint parameter dynamically as the number of training samples increases. In such case the false alarm rate decreases while the miss alarm rate increases gradually for one class SVM. In most applications we prefer a relatively stable performance, especially the false alarm rate. In order to solve that problem, we propose an online version of v-OeSVM. Experiments on toy and real datasets show that v-OeSVM is a good mean to target a given false alarm rate while the AUC increases slowly as the number of new samples increases.