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In semi-supervised learning, a training sample is comprised of both labeled and unla-2 beled instances from each class under consideration. In practice, an important yet challenging 3 issue is the detection of novel classes that may be absent from the training sample. In this article, 4 we focus on a binary situation in which labeled instances come from the positive class whereas 5 unlabeled instances from both classes. Particularly, we propose a semi-supervised large margin 6 classifier todoi:10.5705/ss.202020.0287 fatcat:uab2nvzgabavrgn5k3mtlgvnsi