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Neural Computation, Vol 8, 1085-1106, Copyright © 1996 by The MIT Press

ARTICLES

A numerical study on learning curves in stochastic multilayer feedforward networks

KR Muller, M Finke, N Murata, K Schulten and S Amari
Department of Mathematical Engineering and Inf. Physics, University of Tokyo, Japan.

The universal asymptotic scaling laws proposed by Amari et al. are studied in large scale simulations using a CM5. Small stochastic multilayer feedforward networks trained with backpropagation are investigated. In the range of a large number of training patterns t, the asymptotic generalization error scales as 1/t as predicted. For a medium range t a faster 1/t2 scaling is observed. This effect is explained by using higher order corrections of the likelihood expansion. It is shown for small t that the scaling law changes drastically, when the network undergoes a transition from strong overfitting to effective learning.


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K. Tsuda, S. Akaho, M. Kawanabe, and K.-R. Muller
Asymptotic Properties of the Fisher Kernel
Neural Comput., January 1, 2004; 16(1): 115 - 137.
[Abstract] [Full Text] [PDF]


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Copyright © 1996 by The MIT Press.