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- Title
Information Theoretical Measures for Achieving Robust Learning Machines.
- Authors
Zegers, Pablo; Frieden, B. Roy; Alarcón, Carlos; Fuentes, Alexis
- 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.
- Subjects
PERTURBATION theory; APPROXIMATION theory; ENTROPY; FISHER information; INFORMATION theory
- Publication
Entropy, 2016, Vol 18, Issue 8, p295
- ISSN
1099-4300
- Publication type
Article
- DOI
10.3390/e18080295