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Proceedings Paper

Machine learning in complex systems
Author(s): Travis W. Axtell; Lucas A. Overbey; Lisa Woerner
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Paper Abstract

In this paper, we discuss the design considerations and challenges of using applied machine learning in complex systems, a necessity of operationalizing machine learning techniques. Although many applications of machine learning intend to discern key information insights from large collections of data, in realizable systems the quantity of insights may be so numerous that the insights remain as data and encumber a system and its users. New system design principles are emerging as a result of the dynamism of the machine learning community.

Paper Details

Date Published: 4 May 2018
PDF: 6 pages
Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350B (4 May 2018); doi: 10.1117/12.2309547
Show Author Affiliations
Travis W. Axtell, U.S. Dept. of Defense (United States)
Lucas A. Overbey, Space and Naval Warfare Systems Ctr. Atlantic (United States)
Lisa Woerner, Space and Naval Warfare Systems Ctr. Atlantic (United States)


Published in SPIE Proceedings Vol. 10635:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX
Michael A. Kolodny; Dietrich M. Wiegmann; Tien Pham, Editor(s)

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