Machine learning in complex systems

Travis W. Axtell; Lucas A. Overbey; Lisa Woerner

doi:10.1117/12.2309547

4 May 2018 Machine learning in complex systems

Travis W. Axtell, Lucas A. Overbey, Lisa Woerner

Proceedings Volume 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX; 106350B (2018) https://doi.org/10.1117/12.2309547
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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.

Conference Presentation

Citation Download Citation

Travis W. Axtell, Lucas A. Overbey, and Lisa Woerner "Machine learning in complex systems", Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350B (4 May 2018); https://doi.org/10.1117/12.2309547

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