System and method for determining class discrimination features

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395 21, 395 24, G06E 100, G06F 1518

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active

057347972

ABSTRACT:
A system for generating a minimal artificial neural network (ANN) architecture having as inputs the minimal number of features necessary to discriminate between event classes. A network generator generates an initial ANN architecture. A training processor generates a trained ANN by training the initial ANN to a desired degree of accuracy. A pruning processor prunes the trained ANN by removing interconnections and nodes from the trained ANN. The training processor and pruning processor continue to train and prune the ANN until a minimal network architecture having the class discrimination features as its only inputs is obtained.

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Lursinap "Applied Self-Recovery Technique to Link and Neuron Prunings," Circuits and Systems, 1994 Midwest Symposium, V. 1, pp. 545-548, Dec. 31, 1994.
Luk et al. "Pruning via Number of Links and Neuron Activities," Circuits and Systems, 1993 IEEE International Symposium, pp. 2415-2418, May 31, 1993.
Khorasani et al. "Structure Adaptation in FeedFoward Neural Networks," Neural Networks, 1994 International Conf., vol. 3, pp. 1403-1408, Dec. 31, 1994.
Giles et al. "Pruning Recurrent Neural Networks for Improved Generalization Performance," IEEE Transaction on Neural Networks, vol. 5, Issue 5, pp. 848-851, Sep. 30, 1994.

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