A hybrid active-online learning framework maintains near-ceiling accuracy in optical network failure detection by labeling only 3.4% of streaming samples via margin-based selection.
Failure management in optical networks with ML: A tutorial on applications, challenges, and pitfalls
2 Pith papers cite this work. Polarity classification is still indexing.
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Conformal QoT applies conformal prediction with operational policies to deliver statistically guaranteed QoT estimates and reports accuracy rising from 92% to 99.6% under domain shift on open datasets.
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Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection
A hybrid active-online learning framework maintains near-ceiling accuracy in optical network failure detection by labeling only 3.4% of streaming samples via margin-based selection.
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Policy-driven Conformal Prediction for Trustworthy QoT Estimation
Conformal QoT applies conformal prediction with operational policies to deliver statistically guaranteed QoT estimates and reports accuracy rising from 92% to 99.6% under domain shift on open datasets.