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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 a new regularization approach for Extreme Learning Machine-based Single- hidden Layer Feedforward Neural network training. We show that the proposed regularizer is able to weight the dimensions of the ELM space according to the importance of the network's hidden layer weights, without imposing additional computational and memory costs in the network learning process. This...
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