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Several hundred flight parameters are recorded per second from worldwide commercial aircraft operations, including guidance, navigation, control, avionics, propulsion, and pilot inputs. This dataset is associated with a novel multiple kernel learning algorithm designed to detect safety anomalies in mixed discrete and continuous data streams. The algorithm was tested on real-world aviation data and reportedly uncovered operationally significant events not detectable by other methods.
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