Information theoretical measures for achieving robust learning machines

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Abstract

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.

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APA

Zegers, P., Frieden, B. R., Alarcón, C., & Fuentes, A. (2016). Information theoretical measures for achieving robust learning machines. Entropy, 18(8). https://doi.org/10.3390/e18080295

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