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This paper presents a quick machine learning system inspired by human learning behavior. Data-mining systems based on machine-learning usually need a large number of iterations to acquire correct solutions, whereas people usually find appropriate hidden rules after only a small number of observations of the instances in a dataset. We think that this quick learning is the result of using tentative...
In this study, we extend a minimal resource-allocating network (MRAN) which is an online learning system for Gaussian radial basis function networks (GRBFs) with growing and pruning strategies so as to realize dimension selection and low computational complexity. We demonstrate that the proposed algorithm outperforms conventional algorithms in terms of both accuracy and computational complexity via...
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