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Data in the real world is seldom complete. Occlusions or temporally unavailable sensors often lead to situations where incomplete data is presented for analysis. Approaches to handle incomplete data have been proposed using neural networks such as fuzzy ARTMAP and back propagation. In this paper we propose a novel approach extending the unsupervised neural network based clustering technique called...
Although traditional techniques of machine learning have, in many cases, presented good results, they have been inefficient for data which are constantly expanding and changing over time. To address these problems, new learning techniques have been proposed in the literature. In this paper we propose a technique called ePNN presenting aspects of this recent paradigm of learning. We carried out a series...
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