Removing and adding network connections with recursive-error-minimization equations

Wayne E. Simon; Jeffrey R. Carter

doi:10.1117/12.21210

1 August 1990 Removing and adding network connections with recursive-error-minimization equations

Wayne E. Simon, Jeffrey R. Carter

Proceedings Volume 1294, Applications of Artificial Neural Networks; (1990) https://doi.org/10.1117/12.21210
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

Abstract

One of the key features of Recursive Error Minimization (REM) equations is the efficient computation of the second derivative of mean square error with respect to each connection. The approximate integration of this derivative provides an estimate of the effect of removing or adding connections. A network with a minimum number of connections can then be found for a specific learning task. This has two important consequences. First the explanation of network decisions is much simpler with a minimum net. Second the computational load is a function of the number of connections. Results are presented for learning the English alphabet and for a simpler task learning the first seven letters of the alphabet. 1.

Citation Download Citation

Wayne E. Simon and Jeffrey R. Carter "Removing and adding network connections with recursive-error-minimization equations", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); https://doi.org/10.1117/12.21210

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