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This work develops and tests a neighborhood-based approach to the Gauss-Newton Bayesian regularization trainingmethod for feedforward backpropagation networks. The proposed method improves the training efficiency, significantly reducing requirements on memory and computational time while maintaining the good generalization feature of the original algorithm. This version of the Gauss-Newton Bayesian regularization greatly expands the scope of application of the original method, as it allows training networks up to 100 times larger without losing performance. [ABSTRACT FROM AUTHOR]