Can a student learn optimally from two different teachers

Neirotti, Juan P. (2010). Can a student learn optimally from two different teachers. Journal of Physics A: Mathematical and Theoretical, 43 (1), 015101.

Abstract

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.

Publication DOI: https://doi.org/10.1088/1751-8113/43/1/015101
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: © 2010 IOP Publishing Ltd.
Uncontrolled Keywords: over-specificity in learning algorithms,the supervised,on-line learning scenario,optimal algorithm,approximation,residual generalization error simulations,finite networks,Mathematical Physics,Modelling and Simulation,Statistics and Probability,Physics and Astronomy(all),Statistical and Nonlinear Physics
Full Text Link: http://iopscien ... 21/43/1/015101/
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2010
Authors: Neirotti, Juan P. ( 0000-0002-2409-8917)

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