QUE-scoring via quantum entropy regularization achieves optimal robust mean estimation in Õ(nd) time and outperforms prior outlier detection methods in experiments.
Identification of outliers , volume 11
2 Pith papers cite this work. Polarity classification is still indexing.
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2019 2verdicts
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Applies Dempster-Shafer evidence theory to sensor data fusion for fault detection in IoT nodes, reporting 99.8% accuracy on benchmark data and 99.9% on laboratory data.
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Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
QUE-scoring via quantum entropy regularization achieves optimal robust mean estimation in Õ(nd) time and outperforms prior outlier detection methods in experiments.
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Fault Matters: Sensor Data Fusion for Detection of Faults using Dempster-Shafer Theory of Evidence in IoT-Based Applications
Applies Dempster-Shafer evidence theory to sensor data fusion for fault detection in IoT nodes, reporting 99.8% accuracy on benchmark data and 99.9% on laboratory data.