Publications

Moore, Emily R.; Proudfoot, Oliver S.; Qiu, Henry Q.; Ganter, Tyler G.; Lemon, Brandon L.; Pitts, Todd A.; Moon, Todd K.

The Machine Learning for Correlated Intelligence Laboratory Directed Research & Development (LDRD) Project explored competing a variety of machine learning (ML) classification techniques against a known, open source dataset through the use of a rapid and automated algorithm research & development (RD) infrastructure. This approach relied heavily on creating an infrastructure in which to provide a pipeline for automatic target recognition (ATR) ML algorithm competition. Results are presented for nine ML classifiers against a primary dataset using the pipeline infrastructure developed for this project. New approaches to feature set extraction are presented and discussed as well.

Roberts, Scott A.; Donohoe, Brendan D.; Martinez, Carianne M.; Krygier, Michael K.; Hernandez-Sanchez, Bernadette A.; Foster, Collin W.; Collins, Lincoln; Greene, Benjamin G.; Noble, David R.; Norris, Chance A.; Potter, Kevin M.; Roberts, Christine C.; Neal, Kyle D.; Bernard, Sylvain R.; Schroeder, Benjamin B.; Trembacki, Bradley L.; LaBonte, Tyler L.; Sharma, Krish S.; Ganter, Tyler G.; Jones, Jessica E.; Smith, Matthew D.

Abstract not provided.