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Carnegie Mellon Robotics Institute

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Abstract

The neocognitron is an artificial neural network which applies certain aspects of the mammalian visual process to the task of 2-D pattern recognition. The resulting network model is complex in both structure and parameterization. We describe experiments which show that the performance of the neocognitron is sensitive to certain parameters whose values are seldom detailed in the relevant literature. We also present results which suggest that the selectivity parameters in the neocognitron can be adjusted in a straightforward manner so as to improve the classification performance of the neocognitron.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference

D. Lovell, David Simon, and A. Tsoi, "Improving the Performance of the Neocognitron," Fourth Australian Conference on Neural Networks, 1993, pp. 22-25.

BibTeX Reference

@inproceedings{Simon_1993_1345,
   author = "D. Lovell and David Simon and A. Tsoi",
   editor = "P. Leong and M. Jabri",
   title = "Improving the Performance of the Neocognitron",
   booktitle = "Fourth Australian Conference on Neural Networks",
   pages = "22-25",
   year = "1993",
}

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