Finite size scaling in neural networks
  We demonstrate that the fraction of pattern sets that can be stored in single- and hidden-layer perceptrons exhibits finite size scaling. This feature allows to estimate the critical storage capacity \alpha_c from simulations of relatively small systems. We illustrate this approach by determining \alpha_c, together with the finite size scaling exponent \nu, for storing Gaussian patterns in committee and parity machines with binary couplings and up to K=5 hidden units. 
Walter Nadler (Institut fuer Theoretische Chemie, Universitaet   Tuebingen) and Wolfgang Fink (Institut fuer Theoretische Ph...