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U.S. Air Force Enlisted Classification and Reclassification: Potential Improvements Using Machine Learning and Optimization Models
Sean Robson
(Autor)
·
Matthew Walsh
(Autor)
·
Maria C. Lytell
(Autor)
·
RAND Corporation
· Tapa Blanda
U.S. Air Force Enlisted Classification and Reclassification: Potential Improvements Using Machine Learning and Optimization Models - Robson, Sean ; Lytell, Maria C. ; Walsh, Matthew
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Reseña del libro "U.S. Air Force Enlisted Classification and Reclassification: Potential Improvements Using Machine Learning and Optimization Models"
Although most airmen successfully complete initial skills training, about 10 percent are eliminated and either separated from the United States Air Force or reclassified into other specialties. Given recent increases in reclassification, the authors set out to identify factors that affect initial skills training success and determine how classification and reclassification processes can be improved.
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