paper: Learning and Generalization in Large Committee-Machines

[Riccardo Zecchina - tel.11-5647358, fax. 11-5647399]

FTP-host: archive.cis.ohio-state.edu

The following paper is now available for copying from
FTP-filename: /pub/neuroprose/zecchina.committee.ps.Z

Title: LEARNING A GENERALIZATION THEORIES OF LARGE COMMITTEE-MACHINES
Authors: Remi Monasson and Riccardo Zecchina 
to be appear in Int.J.Mod.Phys.B.

Abstract:
The study of the distribution of volumes associated to the internal
representations of learning examples allows us to derive the critical learning
capacity ($\alpha_c=\frac{16}{\pi} \sqrt{\ln K}$) of large committee machines,
to verify the stability of the solution in the limit of a large number $K$ of
hidden units and to find a Bayesian generalization cross--over at $\alpha=K$.


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E_mail: zecchina at to.infn.it