Finite Size Scaling in Neural Networks
نویسندگان
چکیده
We demonstrate that the fraction of pattern sets that can be stored in singleand hidden-layer perceptrons exhibits finite size scaling. This feature allows one to estimate the critical storage capacity ac from simulations of relatively small systems. We illustrate this approach by determining ac , together with the finite size scaling exponent n, for storing Gaussian patterns in committee and parity machines with binary couplings and up to K 5 hidden units. [S0031-9007(96)02100-X]
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