22 June 2016 Implementing a self-structuring data learning algorithm
Author Affiliations +
Proceedings Volume 9826, Cyber Sensing 2016; 98260G (2016); doi: 10.1117/12.2228743
Event: SPIE Defense + Security, 2016, Baltimore, Maryland, United States
In this paper, we elaborate on what we did to implement our self-structuring data learning algorithm. To recap, we are working to develop a data learning algorithm that will eventually be capable of goal driven pattern learning and extrapolation of more complex patterns from less complex ones. At this point we have developed a conceptual framework for the algorithm, but have yet to discuss our actual implementation and the consideration and shortcuts we needed to take to create said implementation. We will elaborate on our initial setup of the algorithm and the scenarios we used to test our early stage algorithm. While we want this to be a general algorithm, it is necessary to start with a simple scenario or two to provide a viable development and testing environment. To that end, our discussion will be geared toward what we include in our initial implementation and why, as well as what concerns we may have. In the future, we expect to be able to apply our algorithm to a more general approach, but to do so within a reasonable time, we needed to pick a place to start.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James Graham, Daniel Carson, Igor Ternovskiy, "Implementing a self-structuring data learning algorithm", Proc. SPIE 9826, Cyber Sensing 2016, 98260G (22 June 2016); doi: 10.1117/12.2228743; https://doi.org/10.1117/12.2228743


Self-structuring data learning approach
Proceedings of SPIE (June 22 2016)
Visualizing output for a data learning algorithm
Proceedings of SPIE (July 13 2016)
Multiple-model detection of target maneuvers
Proceedings of SPIE (October 04 2005)
Global modeling approach for multisensor problems
Proceedings of SPIE (August 01 1991)

Back to Top