Prototype selection rule for neural network training

Lalit Gupta; Jiesheng Wang; Alain Mozart Charles; Paul Kisatsky

doi:10.1117/12.140131

1 July 1992 Prototype selection rule for neural network training

Lalit Gupta, Jiesheng Wang, Alain Mozart Charles, Paul Kisatsky

Proceedings Volume 1710, Science of Artificial Neural Networks; (1992) https://doi.org/10.1117/12.140131
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Lalit Gupta, Jiesheng Wang, Alain Mozart Charles, and Paul Kisatsky "Prototype selection rule for neural network training", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140131

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