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In this paper, the Semi-Supervised Subclass Support Vector Data Description is presented, a method that operates in both the supervised and the semi-supervised One-class classification case. The proposed method consists a novel extension of the standard SVDD method, by introducing two additional terms its optimization problem. These two terms correspond to expressing global and local geometric data...
In this paper, we apply One-Class Classification methods in facial image analysis problems. We consider the cases where the available training data information originates from one class, or one of the available classes is of high importance. We propose a novel extension of the One-Class Extreme Learning Machines algorithm aiming at minimizing both the training error and the data dispersion and consider...
In this paper, we propose an extreme learning machine (ELM)-based one-class classification method that exploits geometric class information. We formulate the proposed method to exploit data representations in the feature space determined by the network hidden layer outputs, as well as in ELM spaces of arbitrary dimensions. We show that the exploitation of geometric class information enhances performance...
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