Phys. Rev. A 41, 7097–7100 (1990)First-order transition to perfect generalization in a neural network with binary synapses
Learning from examples by a perceptron with binary synaptic parameters is studied. The examples are given by a reference (teacher) perceptron. It is shown that as the number of examples increases, the network undergoes a first-order transition, where it freezes into the state of the reference perceptron. When the transition point is approached from below, the generalization error reaches a minimal positive value, while above that point the error is constantly zero. The transition is found to occur at αGD=1.245 examples per coupling [E. Gardner and B. Derrida, J. Phys. A 22, 1983 (1989)]. © 1990 The American Physical Society URL:
http://link.aps.org/doi/10.1103/PhysRevA.41.7097
DOI:
10.1103/PhysRevA.41.7097
PACS:
87.10.+e, 02.50.+s, 64.60.Cn
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