A metric learning method is introduced to learn distance metrics that best capture conditional anomaly patterns in instance-based detection.
and Kriegel, Hans-Peter and Ng, Raymond T
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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Instance-based conditional anomaly detection with optimized distance metrics detects unusual patient-management decisions in two real-world medical datasets.
GNN embeddings turn CEBAF injector snapshots into a coordinate system revealing ten persistent operating regimes that support monitoring, outlier detection, and case-based reasoning over a year of data.
citing papers explorer
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Distance metric learning for conditional anomaly detection
A metric learning method is introduced to learn distance metrics that best capture conditional anomaly patterns in instance-based detection.
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Conditional anomaly detection methods for patient-management alert systems
Instance-based conditional anomaly detection with optimized distance metrics detects unusual patient-management decisions in two real-world medical datasets.