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Given data at a large set of centers, the problem investigated in this article, is how to choose a smaller subset of centers such that the least squares radial function approximation will be with predefined accuracy. Our solution is based on an adaptive thinning strategy, removing in a greedy way less significant centers one by one so as to minimize an anticipated error. The novelty in our approach...