Priming an artificial neural classifier
Repetition priming capacity enables biological systems to manage easily with recently met situations. Priming an artificial neural network is of great interest in some modeling tasks. The network is an incremental neural classifier. This system creates units when it is not able to recognize a pattern correctly. Repetition priming is introduce through a priming function, by reinforcing the recognition of recently seen categories. Characteristics of this function are discused in order to find the more suitable shape. Experiments are performed on handwritten recognition application. Methods described enable to detect easily priming with low computation (computation of a simple linear regression). More computation enables to measure the phenomenon (difference between the slope of the regression lines, with and without priming).
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- [AA92]A. Azcarraga and B. Amy. An incremental neural classifier of configurations of active orientation-specific line detectors. In Artificial Neural Networks 2, 1992.Google Scholar
- [AG92]A. Azcarraga and A Giacometti. A prototype-based incremental network model for classification tasks. In Proc. Neuro-Nîmes, pages 121–134, November 1992.Google Scholar
- [APMP94]A. Azcarraga, H. Paugam-Moisy, and D. Puzenat. An incremental neural classifier on a mimd parallel computer. In C. Girault, editor, Applications in Parallel and Distributed Computing, volume A-44 of IFIP Transactions, pages 13–22, Caracas, April 1994.Google Scholar
- [CG88]G. Carpenter and S. Grossberg. The art of adaptive pattern recognition by a self-organizing neural network. IEEE Computer, 21(3):77–88, March 1988.Google Scholar
- [KK92]S. M. Kosslyn and O. Koenig. Wet Mind: The new cognitive Neuroscience. The Free Press (ISBN 0-02-917595-X), 1992.Google Scholar