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.
Learning long-and short-term temporal patterns for ml- driven fault management in optical communication net- works
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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.