Can a student learn optimally from two different teachers

    Research output: Contribution to journalArticlepeer-review


    We explore the effects of over-specificity in learning algorithms by investigating the behavior of a student, suited to learn optimally from a teacher B, learning from a teacher B' ? B. We only considered the supervised, on-line learning scenario with teachers selected from a particular family. We found that, in the general case, the application of the optimal algorithm to the wrong teacher produces a residual generalization error, even if the right teacher is harder. By imposing mild conditions to the learning algorithm form, we obtained an approximation for the residual generalization error. Simulations carried out in finite networks validate the estimate found.
    Original languageEnglish
    Article number015101
    Pages (from-to)015101
    Number of pages1
    JournalJournal of Physics A: Mathematical and Theoretical
    Issue number1
    Publication statusPublished - 2010

    Bibliographical note

    © 2010 IOP Publishing Ltd.


    • over-specificity in learning algorithms
    • the supervised
    • on-line learning scenario
    • optimal algorithm
    • approximation
    • residual generalization error simulations
    • finite networks


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