Computational capabilities of multilayer committee machines

Juan P. Neirotti, L. Alberto Franco

    Research output: Contribution to journalArticlepeer-review


    We obtained an analytical expression for the computational complexity of many layered committee machines with a finite number of hidden layers (L < 8) using the generalization complexity measure introduced by Franco et al (2006) IEEE Trans. Neural Netw. 17 578. Although our result is valid in the large-size limit and for an overlap synaptic matrix that is ultrametric, it provides a useful tool for inferring the appropriate architecture a network must have to reproduce an arbitrary realizable Boolean function.
    Original languageEnglish
    Article number445103
    Pages (from-to)445103
    Number of pages1
    JournalJournal of Physics A: Mathematical and Theoretical
    Issue number44
    Publication statusPublished - 5 Nov 2010

    Bibliographical note

    © 2010 IOP Publishing Ltd.


    • computational complexity
    • layered committee machines
    • generalization complexity measure
    • overlap synaptic matrix
    • Boolean function


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