Learning in ultrametric committee machines

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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.



Original languageEnglish
Pages (from-to)887-897
Number of pages11
JournalJournal of Statistical Physics
Issue number5
Early online date21 Nov 2012
StatePublished - Nov 2012

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The original publication is available at www.springerlink.com


  • multilayered networks, learning by examples

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