Learning a spin glass: Determining Hamiltonians from metastable states

S.M. Kuva, O. Kinouchi and N. Caticha

Physica A: Statistical Mechanics and its Applications, 1998, vol. 257, issue 1, 28-35

Abstract: We study the problem of determining the Hamiltonian of a fully connected Ising spin glass of N units from a set of measurements, whose sizes needs to be O(N2) bits. The student–teacher scenario, used to study learning in feed-forward neural networks, is here extended to spin systems with arbitrary couplings. The set of measurements consists of data about the local minima of the rugged energy landscape. We compare simulations and analytical approximations for the resulting learning curves obtained by using different algorithms.

Keywords: Neural networks; Generalization; Spin glasses; Inverse problems; On-line learning (search for similar items in EconPapers)
Date: 1998
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:257:y:1998:i:1:p:28-35

DOI: 10.1016/S0378-4371(98)00126-5

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