Multiclass Multiple-Instance Learning for Predicting Precursors to Aviation Safety Events
Author:
Affiliation:
1. Georgia Institute of Technology, Atlanta, Georgia 30332
2. KBR, Inc., NASA Ames Research Center, Moffett Field, CA 94035
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Aerospace Engineering
Link
https://arc.aiaa.org/doi/pdf/10.2514/1.I010971
Reference22 articles.
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3. GavrilovskiA.JimenezH.MavrisD.RaoA. H.MaraisK.ShinS.HwangI. “Challenges and Opportunities in Flight Data Mining: A Review of the State of the Art,” AIAA Paper 2016-0923, 2016. 10.2514/6.2016-0923
4. Identification of Instantaneous Anomalies in General Aviation Operations Using Energy Metrics
5. Flight track pattern recognition based on few labeled data with outliers
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