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Information Theoretical Measures for Achieving Robust Learning Machines

Information theoretical measures are used to design, from first principles, an objective function that can drive a learning machine process to a solution that is robust to perturbations in parameters. Full analytic derivations are given and tested with computational examples showing that indeed the procedure is successful. The final solution, implemented by a robust learning machine, expresses a balance between Shannon differential entropy and Fisher information. This is also surprising in being an analytical relation, given the purely numerical operations of the learning machine.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/621411
Date12 August 2016
CreatorsZegers, Pablo, Frieden, B., Alarcón, Carlos, Fuentes, Alexis
ContributorsUniv Arizona, Coll Opt Sci
PublisherMDPI AG
Source SetsUniversity of Arizona
LanguageEnglish
Detected LanguageEnglish
TypeArticle
RightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Relationhttp://www.mdpi.com/1099-4300/18/8/295

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