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A shared-weight neural network based on mathematical morphology is introduced. The feature extraction process is learned by interaction with the classification process. Feature extraction is performed using gray-scale hit-miss transforms that are independent of gray-level shifts. The morphological shared-weight neural network (MSNN) is applied to automatic(More)
Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output weights with the random choice of input weights and hidden(More)
Neural networks have been massively used in regression problems due to their ability to approximate complex nonlinear mappings directly from input patterns. However, collected data for training networks often include outliers which affect final results. This paper presents an approach for training single hidden-layer feedforward neural networks (SLFNs)(More)