Learning in ultrametric committee machines

Neirotti, Juan (2012). Learning in ultrametric committee machines. Journal of Statistical Physics, 149 (5), pp. 887-897.

Abstract

The problem of learning by examples in ultrametric committee machines (UCMs) is studied within the framework of statistical mechanics. Using the replica formalism we calculate the average generalization error in UCMs with L hidden layers and for a large enough number of units. In most of the regimes studied we find that the generalization error, as a function of the number of examples presented, develops a discontinuous drop at a critical value of the load parameter. We also find that when L>1 a number of teacher networks with the same number of hidden layers and different overlaps induce learning processes with the same critical points.

Publication DOI: https://doi.org/10.1007/s10955-012-0636-1
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: The original publication is available at www.springerlink.com
Uncontrolled Keywords: multilayered networks,learning by examples,Statistical and Nonlinear Physics,Mathematical Physics
Full Text Link: http://www.spri ... 35847k88l8h18n/
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2012-11
Authors: Neirotti, Juan ( 0000-0002-2409-8917)

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