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In this paper we consider the problem of approximating functions from noisy data. We propose an incremental supervised learning algorithm for RBF networks. Hidden Gaussian nodes are added in an iterative manner during the training process. For each new node added, the activation function center and the output connection weight are settled according to an extended chained version of the Nadaraja-Watson...
By utilizing a new definition of product, we develop a neural net model. The memorization and generalization capabilities are investigated in an Information Theory fashion. To show the memorization capabilities, we use it as a decoder, and prove the net reduces the error probability to zero in the range of the error correcting capacity of the used code. To show the generalization capabilities, we...
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