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...
| Main Authors: | , , , |
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| Other Authors: | |
| Language: | en |
| Published: |
MDPI AG
2016
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10150/621411 http://arizona.openrepository.com/arizona/handle/10150/621411 |