article · Journal of Physics A Mathematical and Theoretical · 2009
Can a student learn optimally from two different teachers?
Cited by 5Open access
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.
Cite this paper
Neirotti, J. P. (2009). Can a student learn optimally from two different teachers? Journal of Physics A Mathematical and Theoretical, 43(1), 015101. https://doi.org/10.1088/1751-8113/43/1/015101
Neirotti, Juan Pablo. “Can a student learn optimally from two different teachers?” Journal of Physics A Mathematical and Theoretical, vol. 43, no. 1, 2009, p. 015101, https://doi.org/10.1088/1751-8113/43/1/015101.
Neirotti, Juan Pablo. 2009. “Can a student learn optimally from two different teachers?” Journal of Physics A Mathematical and Theoretical 43 (1): 015101. https://doi.org/10.1088/1751-8113/43/1/015101.
Neirotti, J.P. (2009) ‘Can a student learn optimally from two different teachers?’, Journal of Physics A Mathematical and Theoretical, 43(1), p. 015101. Available at: https://doi.org/10.1088/1751-8113/43/1/015101.
@article{neirotti2009student,
title = {{Can a student learn optimally from two different teachers?}},
author = {Neirotti, Juan Pablo},
journal = {Journal of Physics A Mathematical and Theoretical},
volume = {43},
number = {1},
pages = {015101},
year = {2009},
doi = {10.1088/1751-8113/43/1/015101}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
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