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Efficient Classification of Polarization Events Based on Field Measurements

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Abstract

We present rare-event classification of polarization transients based on field measurements with data augmentation combined with robot-generated fiber-disturbance data. We compare machine learning methods for accuracy and required number of training sample traces.

© 2020 The Author(s)

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