ISSN: 2357-8483
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ISSN: 2357-8483
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First of all
por Layne Pride (29-08-2025)
Neural networks have been broadly applied for performing duties in crucial domains, equivalent to, for example, the medical area; their robustness is, due to this fact, necessary to be guaranteed. In this paper, we suggest a robustness definition for neural networks used for regression, by tackling a few of the issues of existing robustness definitions. To start with, by following recent works carried out for classification problems, we suggest to define the robustness of networks used for regression w.r.t. Since completely different alteration ranges will not be always equally possible, the robustness definition is parameterized with the likelihood distribution of the alterations. The error done by this type of networks is quantifiable as the difference between the estimated worth and the anticipated value; since not all the errors are equally important, the robustness definition can be parameterized with a "tolerance" function that specifies how the error is tolerated. The present work has been motivated by the collaboration with the industrial companion that has implemented a medical sensor using a Multilayer Perceptron for the estimation of the blood oxygen strain. After having computed the robustness for BloodVitals home monitor the case study, we now have successfully utilized three methods to enhance the community robustness: knowledge augmentation with recombined data, information augmentation with altered data, and incremental studying. All of the techniques have proved to contribute to rising the robustness, although in alternative ways.
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