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<br>Neural networks have been broadly applied for performing duties in vital domains, corresponding to, for instance, the medical domain; their robustness is, therefore, important to be assured. In this paper, we propose a robustness definition for neural networks used for regression, by tackling some of the problems of existing robustness definitions. First of all, by following recent works finished for classification issues, we suggest to define the robustness of networks used for regression w.r.t. Since totally different alteration ranges are not all the time equally probable, the robustness definition is parameterized with the chance distribution of the alterations. The error done by one of these networks is quantifiable because the difference between the estimated worth and the expected worth; since not all of the errors are equally important,  [http://classicalmusicmp3freedownload.com/ja/index.php?title=Given_Many_Repetitions_Of_A_Thought BloodVitals SPO2 device] the robustness definition can also 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 partner that has applied a medical sensor using a Multilayer Perceptron for the estimation of the [http://classicalmusicmp3freedownload.com/ja/index.php?title=%E5%88%A9%E7%94%A8%E8%80%85:VanceHaugh73 wireless blood oxygen check] oxygen stress. After having computed the robustness for the case research, we have now successfully applied three methods to enhance the community robustness: information augmentation with recombined knowledge, knowledge augmentation with altered data, and incremental studying. All of the methods have proved to contribute to growing the robustness, though in alternative ways.<br><br><br><br>Posts from this subject can be added to your each day electronic mail digest and your homepage feed. Posts from this topic will probably be added to your day by day email digest and your homepage feed. Posts from this matter will probably be added to your each day e mail digest and your homepage feed. Posts from this creator will likely be added to your every day electronic mail digest and your homepage feed. Posts from this creator might be added to your every day electronic mail digest and your homepage feed. 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