Empirically Measuring Transfer Distance for System Design and Operation

Classical machine learning approaches are sensitive to non-stationarity. Transfer learning can address non-stationarity by sharing knowledge from one system to another, however, in areas like machine prognostics and defense, data is fundamentally limited. Therefore, transfer learning algorithms have...

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Bibliographic Details
Published inarXiv.org
Main Authors Tyler, Cody, Adams, Stephen, Beling, Peter A
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 02.07.2021
Subjects
Algorithms
Computer Science - Learning
Computer Science - Systems and Control
Computer vision
Machine learning
Performance prediction
Systems design
Systems engineering
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Summary:Classical machine learning approaches are sensitive to non-stationarity. Transfer learning can address non-stationarity by sharing knowledge from one system to another, however, in areas like machine prognostics and defense, data is fundamentally limited. Therefore, transfer learning algorithms have little, if any, examples from which to learn. Herein, we suggest that these constraints on algorithmic learning can be addressed by systems engineering. We formally define transfer distance in general terms and demonstrate its use in empirically quantifying the transferability of models. We consider the use of transfer distance in the design of machine rebuild procedures to allow for transferable prognostic models. We also consider the use of transfer distance in predicting operational performance in computer vision. Practitioners can use the presented methodology to design and operate systems with consideration for the learning theoretic challenges faced by component learning systems.
ISSN:2331-8422
DOI:10.48550/arxiv.2107.01184