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

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.