arXiv Analytics

Sign in

arXiv:cond-mat/9710178AbstractReferencesReviewsResources

Comment on "Finite size scaling in Neural Networks"

M. Schroeder, R. Urbanczik

Published 1997-10-17, updated 1997-12-11Version 2

We use a binary search tree and the simplex algorithm to measure the fraction of patterns that can be stored by an Ising perceptron. The algorithm is much faster than exhaustive search and allows us to obtain accurate statistics up to a system size of N=42. The results show that the finite size scaling ansatz Nadler and Fink suggest in [1] cannot be applied to estimate accurately the storage capacity from small systems. [1] W.Nadler and W.Fink: Phys.Rev.Lett. 78, 555 (1997)

Related articles: Most relevant | Search more
arXiv:2409.13597 [cond-mat.dis-nn] (Published 2024-09-20)
Fluctuation-learning relationship in neural networks
arXiv:2107.01163 [cond-mat.dis-nn] (Published 2021-07-02)
Unveiling the structure of wide flat minima in neural networks
arXiv:cond-mat/9807089 (Published 1998-07-06)
Generic Modeling of Chemotactic Based Self-Wiring of Neural Networks