  Anomaly detection is not an easy problem since distribution of anomalous
samples is unknown a priori. We explore a novel method that gives a trade-off
possibility between one-class and two-class approaches, and leads to a better
performance on anomaly detection problems with small or non-representative
anomalous samples. The method is evaluated using several data sets and compared
to a set of conventional one-class and two-class approaches.
