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This paper presents an incremental learning and model selection method under the virtual concept drifting environments, where their prior distribution of inputs is changing over time. In the previous work, a statistical model of the virtual concept drift was constructed, and the model-selection criterion for radial basis function neural networks (RBFNNs) under such environments was built with the...
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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