Jose Castro

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Fuzzy ARTMAP (FAM) is a neural network architecture that can establish the correct mapping between real-valued input patterns and correct labels in a variety of classification problems. Nevertheless, as the size of the dataset grows to thousands and hundreds of thousands, FAM's convergence time slows down considerably. In this paper we focus on a FAM(More)
A new methodology describing the effects of aperiodic and multiplexed gratings in volume holographic imaging systems (VHIS) is presented. The aperiodic gratings are treated as an ensemble of localized planar gratings using coupled wave methods in conjunction with sequential and non-sequential ray-tracing techniques to accurately predict volumetric(More)
Machine Learning has traditionally been a topic of research and instruction in computer science and computer engineering programs. Yet, due to its wide applicability in a variety of fields, its research use has expanded in other disciplines, such as electrical engineering, industrial engineering, civil engineering, and mechanical engineering. Currently,(More)
Fuzzy ARTMAP neural networks have been proven to be good classifiers on a variety of classification problems. However, the time that it takes Fuzzy ARTMAP to converge to a solution increases rapidly as the number of patterns used for training increases. In this paper we propose a coarse grain parallelization technique, based on a pipeline approach, to(More)
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