  This work introduces the one-class slab SVM (OCSSVM), a one-class classifier
that aims at improving the performance of the one-class SVM. The proposed
strategy reduces the false positive rate and increases the accuracy of
detecting instances from novel classes. To this end, it uses two parallel
hyperplanes to learn the normal region of the decision scores of the target
class. OCSSVM extends one-class SVM since it can scale and learn non-linear
decision functions via kernel methods. The experiments on two publicly
available datasets show that OCSSVM can consistently outperform the one-class
SVM and perform comparable to or better than other state-of-the-art one-class
classifiers.
