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Genetic selection of biologically inspired receptive fields for computational vision

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2 Author(s)
Perez, C.A. ; Dept. of Electr. Eng., Chile Univ., Santiago, Chile ; Salinas, C.

The paper presents the genetic selection of biologically inspired receptive fields classifiers to improve pattern recognition in neural networks. A genetic algorithm is employed to select the x and y dimensions of the receptive fields in a two plane per layer configuration with two hidden layers. Networks were ranked based on the fitness criterion: best generalization performance on handwritten digits. Results show a strong correlation between the neural network performance and the receptive field x and y dimensions. The best receptive field configuration results outperformed those of the perceptron based models. Best receptive field configurations consist of a small aspect ratio in x and y direction in each plane of the two hidden layers

Published in:

[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint  (Volume:2 )

Date of Conference:

Oct 1999