1 July 1992 Prototype selection rule for neural network training
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Abstract
Rules to select a set of training prototypes from a collection of training prototypes are developed so that a neural network classifier converges to a solution when pattern classes overlap in feature space. The formulation of the selection rules are based on distortion measure and the network response to the training prototype collection. The rules are also especially useful for selecting training prototypes in order to improve the network robustness and operational flexibility by retraining the network with noisy prototypes. The application and effectiveness of the selection rules are demonstrated on a synthetic pattern classification in Gaussian noise problem and a practical automatic target recognition problem.
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Lalit Gupta, Lalit Gupta, Jiesheng Wang, Jiesheng Wang, Alain Mozart Charles, Alain Mozart Charles, Paul Kisatsky, Paul Kisatsky, } "Prototype selection rule for neural network training", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140131; https://doi.org/10.1117/12.140131
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